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We played the Modern Warfare 4 beta this weekend — including Warzone and the new Zodiac Resurgence map — and it took about fifteen minutes to know it was over. Not "this year is mid" over. Over over. This is the episode where a podcast that ran on Call of Duty for 600+ episodes says out loud that it's done. Raz makes the case that nothing else in an FPS matters if you can't see or hear the enemy, and everything else — weapon levels, perks, pipe climbing, the spawns everyone's arguing about on X — is fixing a leaky bathtub while the house burns down. Jake breaks down why CoD only takes from him now, why the lobbies are nothing but sweat, and the exact point where min-maxing your controller, your router and your ZIP code stopped being worth it. Also in this one: the Zodiac verdict (better than Area 99, roughly Haven's Hollow), the weekend 2 nerf pass, the 60% negative Steam reviews, why Warzone was in the beta at all, and a DMZ 2 prediction we're standing behind. Then a palate cleanser — Star Wars Zero Company, permadeath, and a first playthrough Raz completely ruined — plus a brutal Tarkov week, the Tarkov roadmap, GTA 6's Netflix look, and WARDOGS. Jake wanted it to be good. Raz stopped wanting that years ago. This time they agree. 0:00 - Intro 1:10 - New patrons and the latest bonus episodes 3:26 - Our first patron Discord hangout (and Death by AI) 6:10 - "The MW4 Beta Broke Us" 6:22 - How fast did we know? 9:25 - "If I can't see the guy, nothing else matters" 10:17 - The raging inferno analogy 14:55 - "Call of Duty is dead" 15:10 - Jake: CoD only takes from me now 20:49 - Everyone is min-maxing (and moving for zero ping) 23:26 - SBMM, the missing casuals, and sweat-only lobbies 26:03 - Splitgate Reloaded and the death of 6v6 arcade shooters 27:40 - Warzone: the audio, the visibility, the haze 30:12 - Rolling the dice on what bullshit you get this year 31:28 - Bring back Caldera — at least it was different 37:28 - The first thing that felt wrong: audio 37:54 - Weekend 2's nerf pass: Tac Sprint, slides, TTK 40:44 - 60% negative Steam reviews and the player drop 41:52 - The Zodiac verdict vs. Area 99 and Haven's Hollow 42:50 - Why was Warzone in the beta at all? 47:16 - No custom loadouts, and the guns aren't fun to shoot 49:07 - "Activision devs do not play their own game" 50:18 - The one dev we ever ran into in Warzone 53:34 - The Quick Fix perk they "fixed" twice 57:07 - "Where does that put us? I give you Zodiac" 59:12 - Skipping a year, or actually done? 1:04:14 - What it would take to get us back 1:07:00 - Call of Duty is your old restaurant job 1:08:08 - "I want everyone that works there to suffer" 1:09:00 - It's a leadership problem — fire Infinity Ward 1:13:02 - Prediction: MW4 three months after launch 1:16:06 - The DMZ 2 prediction 1:24:17 - Palate cleanser: Star Wars Zero Company 1:32:45 - Permadeath, injuries, and squad bonds 1:34:36 - "This playthrough is fucked" 1:36:57 - Is it the Star Wars or the game underneath? 1:37:51 - Reviews, performance, and is it worth $50 1:38:50 - Eight classes, Overwatch combos, and base upgrades 1:44:46 - It should have been co-op 1:45:49 - Tarkov: best raid of the week 1:46:41 - The Customs transit raid that actually worked 1:50:02 - Worst raid: baited in the Reserve bunker 1:53:04 - The KS-23 rat that bled us out 1:54:39 - Camped at D2 twice in a row 1:59:49 - The bathroom extract death 2:02:34 - Why this wipe feels so much harder 2:05:18 - Tarkov roadmap: ranked, revives, simplified GPS 2:08:24 - GTA 6's Netflix extended look 2:10:31 - WARDOGS extraction and PvE prototypes 2:11:00 - Wrap-up _Note: timestamps may be slightly misaligned on podcast apps (but not on YouTube) due to dynamic ads._ The podcast is available wherever you listen to podcasts, and ad-free & early access versions - as well as bonus episodes - are available to all of our Patreon (https://www.patreon.com/thedropshot) supporters. We stream the podcast live on our YouTube (https://www.youtube.com/c/thedropshotpodcast) every Saturday morning at ~9 o'clock Pacific Time. We typically start the stream 30 minutes early to answer viewer questions, banter, and chat. Links for everything are below. Thanks for checking us out!
We discuss what we would change about our lifestyle journey if we had a time machine and could go back 8 1/2 years... Spoiler alert, we have very different answers! Plus, a deep dive into what lifestyle friendships ACTUALLY mean to us now. Schedule your free consultative call with us at https://betterbedroombody.com Support the show and receive bonus episodes of FPS at https://patreon.com/frontporchswingers Get 10% off your first Promescent order at https://promescent.com/fps Try Kasidie FREE for a month! Click on the Kasidie banner at https://frontporchswingers.com
Try Factor at https://www.factormeals.com/fps50off with code fps50off to get 50% off your first box plus free breakfast for 1 year. Thanks to Factor for sponsoring FPS! -- Timestamps: 00:00 Intro 9:10 GTA 6 Extended Look Discussion 26:14 Factor (ad) 28:35 Lucy's seen The Witcher 3: Songs of the Past 40:27 Ralph's played Marvel's Wolverine 48:06 Jake's been playing Metal Gear Solid Master Collection 2 59:14 Jake and Ralph have been playing Star Wars: Zero Company 01:14:42 Lucy's played Stranger Than Heaven and Persona 4 Revival 01:23:58 Ralph's been playing Elden Ring: Tarnished Edition 01:32:57 Jake and Ralph have been playing Hyperdrop 01:38:12 Lucy's been playing Blood Dungeon 01:42:02 Show and Tell 01:54:07 Wrap Up -- Listen to the Friends Per Second Podcast on your favourite podcast platform: https://linktr.ee/friendspersecond Follow on Instagram: https://www.instagram.com/friendspersecond -- Let's meet our hosts! - Jake Baldino (aka the Before You Buy Guy) is pretty much the most watched reviewer on YouTube across both Gameranx and his personal channel (https://www.youtube.com/c/JakeBaldino). If you're obsessed with Delorians, The Mummy and Pizza you can discuss that stuff with him directly over on Twitter: @JakeBaldino - Lucy James was a Senior Producer at Gamespot for more than a decade before striking out on her own, founding https://lookingfor.game/, a newsletter and games discovery platform that serves up cool suggestions (and demo codes) directly to your inbox. She also co-hosts the annual Summer Games Showcase alongside Geoff Keighly. - Skill Up used to work at McDonalds but he got fired for skimming too many chicken nuggets. He says he regrets it since he hasn't had a better job since. Learn more about your ad choices. Visit megaphone.fm/adchoices
We're joined by WARDOGS Game Directors: Howard Philpott & Mark "phantasy' Pinney this week to talk about filling a much needed niche in the FPS genre, why early access and plenty of viewer questions! Afterwards we chat about the awkwardness that is DLSS5, the GTA6 reveal that broke the internet, and a Witcher 3 remaster in the middle of a massive ton of game releases. Then our time with the new STALKER 2 update, Star Wars Zero Company and more! 00:00:00 - Intro00:01:00 - Can't handle all of these games00:02:40 - Wardogs devs join us00:04:50 - Why early access?00:09:40 - First convention00:17:00 - Graphical advantages00:20:50 - Vehicle control options00:33:00 - Comparisons to Planetside00:43:40 - Private servers00:57:30 - Progression systems01:33:20 - Global players01:39:50 - What have the Wardogs devs been playing?01:50:30 - Announcements at FPS Game Show01:52:30 - The DLSS5 issue01:57:40 - GTA602:11:15 - The Witcher 3 Remaster02:16:40 - STALKER 202:20:00 - Star Wars Zero Company02:44:15 - Wolcen 202:49:10 - Path of Exile 2 - Swords/release date02:51:10 - ShoutoutsSee omnystudio.com/listener for privacy information.
Rockstar's Grand Theft Auto VI gameplay reveal shows the enormous scale of Leonida, including reports of an 80-hour playthrough and a map roughly three times the playable size of Red Dead Redemption 2. We also debate GTA VI's potential 30 FPS target and whether Rockstar gets more leeway than other developers. Microsoft's next generation comes into focus as Xbox details its new disc-to-digital entitlement program and Project Helix is described as a “family of devices.” We break down what that could mean for physical games, Xbox Play Anywhere, future consoles, the $899 Xbox Series X25, the ROG Xbox Ally X20, and rapidly rising gaming hardware prices. Plus: The Witcher 3 Remastered and Songs of the Past, what Bethesda needs to change for The Elder Scrolls VI, Marvel's Iron Man gameplay leak, Aliens: Fireteam Elite 2 impressions, and more from Gamescom 2026. CHAPTERS 00:00 Introduction 11:44 Aliens: Fireteam Elite 2 Impressions 20:56 The Games Getting Buried This Fall 28:41 GTA VI Gameplay Is Finally Here 37:08 Does GTA VI Need 60 FPS? 50:23 What Does Elder Scrolls VI Need to Change? 52:30 The Witcher 3 Returns Again 1:05:56 Xbox's Disc-to-Digital Future 1:09:30 Xbox Series X25 & New Hardware 1:19:30 Project Helix: A “Family of Devices” 1:27:24 Marvel's Iron Man Gameplay Leaks 1:37:30 Gaming Hardware Is Getting Too Expensive
On this week's episode of the Nintendo Dads Podcast: News ● New Nintendo Switch 2 bundles announced ● LEGO Super Mario sets have officially been revealed ● Other Nintendo updates from Gamescom ● Playdate Passing Tariff Refunds to Customers ● 2K Games confirmed NBA 2K27 will play at 60 FPS in Performance Mode on Nintendo Switch 2 ● Game Releases/Updates Let's Discuss ● Last week's Controller Discussion sparked an idea from Jiggy Collector ● This past week, we lost a few celebrities that hit a little close to home Games we've been playing ● The Duskbloods Network Test ● ReStory: Chill Electronics Repairs ● Heroes of Might and Magic III: Remake ● Super Mario Sunshine ● Mega Man 7 ● LEGO Build Update Check out our website at http://nintendodads.org for our latest videos, episodes, tweets, and social media links. Apple Podcasts feed: https://podcasts.apple.com/us/podcast/nintendo-dads-podcast/id950582320?mt=2 YouTube Music feed: https://music.youtube.com/playlist?list=PLyID_QWdPfjM17EE3cg8Pin30jHkLqWKr Spotify feed: https://open.spotify.com/show/3SACicqRHT2yxC9mlUP9PL Become a patron and help us improve the show! https://www.patreon.com/NintendoDads Learn more about your ad choices. Visit megaphone.fm/adchoices
Danny Peña and Parris Lilly break down all the major announcements from NVIDIA's GeForce event at Gamescom! From the rollout of DLSS 4.5 Ray Reconstruction to the new RTX Spark laptops and GeForce NOW upgrades, here is everything you need to know about the future of PC gaming graphics.(Sponsored segment by NVIDIA GeForce)In this episode, we cover: DLSS 4.5 & Ray Reconstruction: Upgrades to lighting, image quality, and AI denoiser performance powered by NVIDIA's 2nd generation transformer model. Supported Titles: First look at DLSS 4.5 implementation in 007 First Light, Control Resonate, Cyberpunk 2077, and Pragmata. RTX 50 Series Specs: Dynamic multi-frame generation and hardware-level performance features. RTX Spark Laptops: Next-gen portable gaming performance offering 1440p at 100+ FPS. RTX Remix & Mods: Community projects getting DLSS 4.5 upgrades, including Painkiller RTX and Morrowind RTX. NVIDIA ACE & Cloud Gaming: AI-powered teammates via NVIDIA ACE and new GeForce NOW features, including Steam Machine support. Connect with us: Danny Peña Parris Lilly Pete Toledo Riana Manuel-Peña Send us questions - fanmail@gamertagradio.com | Speakpipe.com/gamertagradio or 786-273-7GTR. Join our Discord - https://discord.gg/gtr chat with other GTR community member.
Two shooter betas dropped on the same weekend, so we played both and came back with very different verdicts. WARDOGS is Bulkhead's 100-player, three-team tactical shooter with a real money economy — you buy your loadout every respawn, you lose money when you die, and it turns out that changes how a match actually feels. Modern Warfare 4 finally dumps omnimovement, removes bloom from hipfire, and put a campaign mission in the beta for the first time ever. We get into the WARDOGS recon grind and the 5.56 break-action "sniper" that shouldn't exist, the money system and whether it creates real gear fear, three-team fights and whether they actually matter, spawn vehicles as an infinite money glitch, base building and anti-air, and why there is never — not once, not ever — an unoccupied tower. On the CoD side: how the movement feels without omnimovement, no-bloom hipfire on keyboard and mouse, opening-night demon lobbies, whether Warzone and DMZ2 have a chance, and the annual spawns discourse that needs to die. Jake played both. Raz refused to pre-order. Somehow they still ended up disagreeing. 0:00 - Intro 2:26 - How to even get into these betas 2:50 - "I'm not pre-ordering this pile of shit game" 3:23 - Patreon, the FPS lore bonus episode, and the patron hangout 6:09 - Gut check: which beta did we keep loading back into? 6:39 - Jake played the MW4 campaign mission first thing in the morning 8:10 - Opening night of the CoD beta was as sweaty as it gets 11:09 - The WARDOGS recon grind is making the game worse 12:22 - The 5.56 break-action "sniper" that should not exist 13:31 - Two gripes: no free look, and you can't reload while sprinting 15:10 - Recon 10, the Mosin, and a $50,000 unlock 16:41 - There's loot on top of the towers — finding an SV-98 17:44 - What surprised us 18:43 - MW4 movement with no omnimovement 21:48 - So what's actually new in MW4? Pipes and underhand grenades 23:21 - Keyboard and mouse, and the no-bloom hipfire 25:26 - Did we quit a session annoyed? Demon lobbies on day one 26:36 - Ground War and looser SBMM next weekend 27:21 - "The MW4 beta had more concurrent players than BO7's peak" 28:20 - Spawn vehicles and the infinite money glitch 30:25 - Learning to fly the helicopter (badly) 32:19 - Chopper runs are the easiest money in the game 33:50 - Base building, FOBs and anti-air 35:26 - Best moment of the weekend: spawn camping a Lone Star vehicle 36:36 - Pro tip: pack a second optic and hot swap 37:58 - Claymores and tower loot 38:53 - Class leveling and why the grind is a good thing 40:06 - Tanks, attack helicopters, and getting strafed off a roof 43:56 - Do three teams actually change anything? 47:36 - The money system: did it change how you played? 52:01 - Gear fear — "I'm here to PvP" 53:59 - We want there to be consequences to dying 56:06 - Bandages, revives, and running out of resources 58:38 - The crane fight and the parachute escape 1:01:31 - There is never an unoccupied tower 1:03:04 - Shotguns, SMGs, and the two-primary backpack build 1:05:42 - Could a kit ever get too expensive to fight over? 1:09:00 - Armor, AP rounds, and no hit markers 1:10:27 - The SKS chamber bug 1:11:46 - Tac sprint in WARDOGS is driving us insane 1:13:41 - Proning while ADS, and other small friction 1:14:42 - People who refuse to learn a new game 1:19:07 - Who is WARDOGS actually for? 1:21:20 - You can't win if everyone's Bob the Builder 1:22:28 - The one type of player this game won't work for 1:24:39 - Give it a real try before you refund it 1:26:53 - Hesco barriers and why building matters 1:27:18 - MW4 first impressions: movement and gunplay 1:28:38 - The annual spawns discourse needs to stop 1:33:36 - Warzone and DMZ2: the fundamentals are finally sound 1:34:29 - "Will they be good? No" — the prediction 1:35:42 - "It just looks like MW2" is a lazy take 1:37:35 - We are done talking about graphics 1:40:44 - What we're testing next weekend 1:41:10 - Should you buy WARDOGS? And the refund loophole 1:42:11 - Patreon, the hangout, and outro _Note: timestamps may be slightly misaligned on podcast apps (but not on YouTube) due to dynamic ads._ The podcast is available wherever you listen to podcasts, and ad-free & early access versions - as well as bonus episodes - are available to all of our Patreon (https://www.patreon.com/thedropshot) supporters. We stream the podcast live on our YouTube (https://www.youtube.com/c/thedropshotpodcast) every Saturday morning at ~9 o'clock Pacific Time. We typically start the stream 30 minutes early to answer viewer questions, banter, and chat. Links for everything are below. Thanks for checking us out!
We've all seen it in the lifestyle: The dreaded "steering wheel couple," where one half is SIGNIFICANTLY better put together than the other. And it has us wondering: Don't these people want more from their partners? We share intimately times when we didn't feel comfortable holding each other to a higher standard in our relationship, and the toll it took... Let's book a call to talk about your health and wellness goals today! https://betterbedroombody.com Get 15% off Shivers gummies with code FPS at https://shivers.store Get 10% off Promescent's Delay Spray and their other amazing bedroom products at https://promescent.com/fps Support the show and receive bonus episodes on video! https://patreon.com/frontporchswingers
Wolf64 is a port, in pure 6502 assembly, of the 1992 FPS classic to the Commodore 64.More from The Lunduke Journal:https://lunduke.com/ This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit lunduke.substack.com/subscribe
We took Better Bedroom Body to a hotel takeover in Atlanta recently. The experience was WILDLY successful for us from a professional perspective, but personally, we were left asking one important question: "Isn't there more to this?" We explain that, plus share some highlights from our Atlanta trip! Ready to get started on your Better Bedroom journey? Let's set up a call today! https://betterbedroombody.com Get 10% off your first Promescent order at https://promescent.com/fps Get 15% off your first order of Shivers gummies with code FPS at https://shivers.store Join us for our monthly hotwife event here in Las Vegas! https://members.frontporchswingers.com Try Kasidie FREE for a month! Click on the Kasidie banner at https://frontporchswingers.com
Call of Duty | Wardogs | Modern Warfare 4 | Arena Breakout Infinite | Gray Zone Warfare More FPS News #podcast #gaming #fps Welcome to "The Scope," your ultimate FPS gaming podcast! Join us for the latest news, trends, and updates in the world of First Person Shooters. Whether you're a seasoned player or just starting out, our passionate hosts cover everything from new releases to gaming strategies. Dive into the action-packed universe of FPS games with us!Buffnerd GamingChannel: https://www.youtube.com/channel/UCUv67t-1w4i5NJhG3T1vtmgTwitter: https://twitter.com/BuffNerdGaming1BlueTheRobot: Channel: https://www.youtube.com/c/BlueTheRobotTwitter: https://twitter.com/bluetherobotCrash:Discord: https://discord.gg/4HZxRx3MkFTwitch: https://www.twitch.tv/crash8 Twitter: https://twitter.com/fps_crash
A recent consultation with a plastic surgeon has led to some very interesting conversations between the two of us about "vanity" in the lifestyle, and when it makes sense to "fix" something you don't like about yourself. Plus, are you curious which surgery Brenna is looking into next? Chat with us about your own health and wellness goals with a free consultation call at https://betterbedroombody.com Get 15% off your first order of Shivers Gummies with code FPS at https://shivers.store Try Kasidie FREE for 30 days! Click on the Kasidie banner at https://frontporchswingers.com Check out our bonus episodes and support the show at https://patreon.com/frontporchswingers Join us for our monthly Vegas hotwife night! Info at https://members.frontporchswingers.com
I interviewed Richard Turco about One True Path, Part 1 on Tuesday, September 2, 2025 at Venice Immersive in Venice, Italy. Here is the story synopsis for One True Path, Part 1: "The viewer can travel through three fantastical worlds from author Jack Stevenson's oldschool Fiendish Fates gamebook series, each featuring different themes and gameplay. With a magical sword in hand, the viewer can make their way through the necromantic Army of Evil, led by the demonic Grey Knight. In Butchers of Orion, they can use mind-powers and body-hopping abilities to escape the slaughterhouse spaceship of the lifesucking Lady Oriana. Moreover, it is possible to blast the corrupt guards of Sheriff Cassidy's wasteland penitentiary with shock-hand and trusty cyber-shooter in Prisoner of the Cyberhounds. Exploiting interdimensional portals to cross between place, time, and gameplay styles, the viewer discovers clues, secrets, and personal notes to decipher the story behind the books and the true meaning of their journey through Jack's fictional worlds." Here are the contextual domains that are explored: Adventure into speculative literary worlds [9] of a variety of different genres in order to investigate and solve [9] a mystery [12] of why the author was killed [8] as he created these worlds Here is the Elemental Center of Gravity: 1st Center of Gravity of Fire Element / Active Presence: Gameplay Modalities in Different Genres - Mashing those up, uses the waypoint locomotion as a constraint. The most game-like exploration of different actions that are also mirroed in the worldbuilding and story that's being told2nd Center of Gravity of Earth Element / Environmental and Embodied Presence: Embodied Actions that change depending upon the genre of game/literature being explored. These genres are blended in the culmination. There's also the spatial context of the builetin board that helps spatially track where you are in the story.3rd Center of Gravity of Water Element / Emotional Presence: Context of a Mystery. But there's a murder that's happened within the base reality of this world, but then you go off onto these various speculative worlds based upon these different genres of interactive books. This overall experience is just the pilot, and so we don't get to see the full arc of the story. So it's sort of like watching the first episode of a longer series, which leaves it in a bit of a cliffhanger type of situation in terms of fully solving the murder mystery.4th Center of Gravity of Air Element / Mental and Social Presence: Very specific inspiration from literature, so it's an adaptation of sorts. There's cut scene contextualization at the beginning and end of each core gameplay loop cycle where you get a bit more dialogue. But it's mostly a sort of puzzle like game, and so with the constrained locomotion, it puts more of an emphasis on strategy of teleportation movement rather than the brue force nature of FPS shooters. Archetypal Themes and Character Explored: Exploration, Adventure, Gameplay, Solve mystery Artist Statement: "It's a difficult moment for narrative driven VR games and the game industry in general. Despite this, we wanted to push the boundaries of storytelling and develop an ambitious universe, at a time where we don't see lots of innovation in the VR market, especially on the narrative side. We've been developing this first part of the game for several months, with most of the final features included, and decided to release it on early access. Our character Jack has more cards in his sleeve and there will be many more secrets (and even worlds!) to discover. We consider it as a “pilot”, a first part to what will hopefully — with players' help — become the complete narrative experience we've imagined." https://www.youtube.com/watch?v=E2U8p6mEwmI This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality
In "honor" of the release of Halo Campaign evolved the Game Treasure Crew has FINALLY decided to touch the history of Microsoft in the gaming sector with our Console Carousel of Games for the OG Xbox. Also, spoilers, we've got a group review for said Halo Campaign Evolved featured at the end of todays episode. So join the boys as they count off their 5 favorite games, and see whether or not one of histories finest FPS campaigns has been butchered once again or not on another hilarious episode of The Game Treasure Podcast!Thank you so much for watching or listening to The Game Treasure Podcast, we hope you enjoyed! If you'd like to reach out to us, feel free to comment or even email us at gametreasurepodcast@gmail.com.Go to https://www.retrogametreasure.com/ to make your profile and start collecting physical games, today!https://linktr.ee/TheGTP
Watch the full episode on YouTube:We first covered Baseten last year when DeepSeek mania was at peak hype. Now they have raised a monster $13B round and become one of the new cohort of AI Infra decacorns that are (with Nvidia, Intel, and the semis complex) chief beneficiaries of the Inference Inflection. We return to Baseten at the peak of the 2026 edition of Open Weights debate. Ali has published a viral breakdown of Kimi K3:And since you last saw him, Philip has spoken at AI Engineer and written the definitive book on Inference Engineering spotted all over SF:Three years ago, inference engineering barely existed as a category.Today, it is one of the most critical disciplines in AI. Inference engineering inherently tackles a different question than standard model training: “How do you turn those weights from training into a product that is fast, reliable, and affordable at scale?” Focusing on these creates an entirely new optimization problem.In one recent GLM-5.2 experiment, quantizing more of the model actually preserved its benchmark quality while increasing throughput by 20%, because the errors introduced in different layers could cancel each other out.Inference is no longer just the final step after training. It is becoming its own engineering discipline, with its own research problems, infrastructure, and increasingly specialized roles.In this episode, Baseten's Philip Kiely and Ali Taha join swyx and Vibhu to explain what actually happens after a new open model is released and what it takes to turn “we generated a token” into a fast, reliable, production-ready API.We go deep on cache-aware routing, disaggregated prefill and decode, quantization, speculative decoding, KV-cache movement, model parallelism, GPU kernels, and the race to make frontier models up to 10× faster. Philip and Ali explain why inference optimizations can still produce gains of 20%, 100%, or even 200%; how quantization errors can cancel one another out; why identical weights can behave differently across clusters; and how Baseten grafted a Kimi vision encoder onto GLM-5.2 without changing the underlying language model.The conversation then expands beyond LLMs into NVIDIA Dynamo, mega kernels, Rubin, AI-specific chips, local inference, video generation, diffusion versus autoregressive models, and the enormous compute barrier to generating coherent long-form video. Finally, we explore the convergence of training and inference, continual learning through persistent KV cache, and the emerging loop where models help optimize the infrastructure that runs them.We discuss:* What happens when a 200,000-token request enters an inference system* Cache-aware routing and reusing previously computed KV cache* Why prefill and decode are increasingly handled by different GPUs* When dedicated deployments become cheaper and more reliable than shared APIs* How speculative decoding uses a smaller model to accelerate a larger one* Tool calling, structured outputs, and what LLMs actually do* What it takes to support a new open model on day zero* Grafting Kimi's vision encoder onto GLM-5.2* Retrofitting inefficient model layers with components from other architectures* Why models sometimes collapse into repeating the same token* How hardware, kernels, and race conditions create nondeterministic failures* Preserving model fidelity while making inference faster* How quantization errors can cancel each other out* Why inference optimizations still deliver gains of 20%, 100%, and 200%* How optimized serving can make a model up to 10× faster* NVIDIA Dynamo, KV-aware routing, and distributed model serving* Speculative decoding the speculative decoder* Why local AI is about making models less dumb while data-center AI is about making them less slow* Tensor, expert, and pipeline parallelism across GPUs* Hardware-aware model design, auto-tuning, and the case against mega kernels* Rubin and why inference is becoming a systems problem* Whether modern GPUs are evolving into programmable AI ASICs* Why enormous models like Kimi K3 require GB300-class hardware* Why open-source video generation still trails Veo, Kling, and other closed models* The quadratic attention bottleneck behind long-form AI video* Autoregressive video, real-time generation, and compounding quality drift* Why future video systems may combine autoregressive and diffusion architectures* Training for inference and inference for training* Continuous post-training, deployment, evaluation, and improvement loops* How GLM-5.2 helped optimize the kernels serving GLM-5.2 itself* Why faster networking could unlock dramatically faster decoding* Continual learning, KV-cache compaction, and persistent model memoryShow Notes* How to build a day-0 API for Kimi K3* 22580: From GPT2 to Kimi3, ExplainedPhilip Kiely* LinkedIn: https://www.linkedin.com/in/philipkiely* X: https://x.com/philipkiely* Inference Engineering: https://www.baseten.co/inference-engineering/Ali Taha* LinkedIn: https://www.linkedin.com/in/aliestaha/* X: https://x.com/waterloointernTimestamps00:00:00 Introduction and the 200K-Token Prompt00:03:18 Dedicated Deployments, Speculative Decoding, and Tool Calling00:11:26 Launching Production-Ready Open Models00:19:06 Model Retrofits, Failure Modes, and Nondeterminism00:28:22 Quantization and Canceling Errors00:32:15 The Race to 10× Faster Inference00:40:48 Dynamo, Speculation, and Local vs. Data-Center AI00:50:18 Model Parallelism, Auto-Tuning, and Mega Kernels01:00:55 Rubin, GPUs vs. ASICs, and Custom AI Chips01:10:03 Giant Models and the Limits of GPU Memory01:12:42 AI Video, Quadratic Attention, and Autoregressive Generation01:21:47 Audio, Images, and Diffusion Models01:27:32 Training, Self-Optimizing Models, and Continual Learning01:40:06 Closing ThoughtsTranscriptIntroduction: Baseten, Waterloo Intern, and Inference EngineeringSwyx [00:00:00]: Okay, we're here in the studio with Philip, old friend from Inference Engineering, the book, as well as Baseten and everything that you've done, you and I have done before, as well as Ali. Welcome.Ali [00:00:15]: Pleasure to meet you.Swyx [00:00:15]: Waterloo intern.Ali [00:00:16]: Waterloo intern, always.Swyx [00:00:17]: When did you get “Waterloo intern” as a handle?Ali [00:00:19]: As a handle? Oh.Ali [00:00:20]: I think the rebranding happened mid-March. When I saw it was open, I was like, “I have to take it. Up for grabs.”Philip [00:00:26]: The problem is that Ali is really good at his job and is not gonna be an intern much longer.Philip [00:00:30]: So we have to figure out who's gonna get the handle.Ali [00:00:33]: Well, I'll pass the torch over to the next intern.Swyx [00:00:34]: Oh, okay. It can be, like, you just pass it to another Waterloo grad.Ali [00:00:37]: To another Waterloo intern. No, bruh.Philip [00:00:39]: Yeah.Ali [00:00:39]: Intern.Swyx [00:00:40]: Intern, yeah.Ali [00:00:40]: And no.Philip [00:00:41]: You gotta get an intern from Waterloo.Ali [00:00:42]: Yeah, I've gotta get an intern from Waterloo.Swyx [00:00:44]: Right.Ali [00:00:44]: But they have to follow the path.Swyx [00:00:45]: Oh, it could, but it could come from Baseten, so it's like whoever Baseten gets from Waterloo.Ali [00:00:48]: Right.Swyx [00:00:49]: Has the title of Waterloo.Ali [00:00:50]: It stays in the ecosystem.Philip [00:00:51]: Exactly.Ali [00:00:52]: Halfway through the internship, you either get it or you're out.Philip [00:00:55]: You should also do, like, a big graduation ceremony where you change the handle.Ali [00:00:59]: Just say it.Philip [00:00:59]: For everybody.Swyx [00:01:00]: You guys are good at ceremonies, clearly. We had a nice launch of the book, very successful. But before we get into all that, I wanna start off with a fun question for you. Okay, you're an expert inference engineer. What happens when I send a long query, say two hundred thousand tokens into Baseten's inference? What's the process of query through GPU model routing, balancing, all that? What is all the stuff that we don't think about?Long Context Requests, KV Cache, and Cache-Aware RoutingPhilip [00:01:26]: With a long query specifically, the first thing that I'm gonna ask is, “Have you sent me this query before, or at least part of it?” and I really hope you have, because it's gonna be a lot easier for me and a lot cheaper for you. So the first thing that we're gonna look at is some cache-aware routing, where we're going to see, we probably have a number of instances, a number of replicas up serving whatever model you're hitting. We want to send this one to something with, number one, available prefill workers, and number two, ideally some cached input already there so that we can skip prefill on at least part of these two hundred thousand tokens. If you're doing two hundred thousand tokens, it's probably coding or a multi-turn agent or something where you would expect to have that cached. If you don't, we're gonna have to send it to a prefill worker. We've at least on certain models disaggregated prefill and decode, so you're going to have one set of GPUs that's solely going to process the input, create the KV cache, and get you your first token, and then that's going to be passed over to a separate set of GPUs, which is going to run decode. We're going to iteratively make those tokens. We're probably going to have some speculator model in front of that. I'm going to assume that you're doing coding, and because of that, our speculator model, which assumes you're doing coding, is gonna have a high draft token acceptance rate. If I'm wrong and you're asking me to summarize every Harry Potter book, it's gonna be slower. And then we stream that output to you and account for it, charge you, a couple of pennies and say, “Hey, would you like to send another one?”Swyx [00:03:04]: Except Baseten doesn't charge by pennies.Philip [00:03:07]: Well, yeah, we charge. I'm assuming that we're talking about the public model APIs. If you are setting up a dedicated deployment, then yeah, it's not pennies.Public APIs vs. Dedicated DeploymentsSwyx [00:03:18]: Yeah, one of the key differentiators when I was talking with Baseten initially was that people who want very high volume just need to rent by the box, ‘cause then it's up to you to figure out how to saturate the box.Ali [00:03:31]: And more often than not, it's, like, way cheaper if you're pushing, like, millions of tokens per hour, if you just pay per hour instead of pay per token.Philip [00:03:37]: Yeah, they do. I think that we've increasingly seen a lot of demand for the pay per token APIs, just because everyone wants to try open models, and then once they find a use case that's really sticky, then they move over to dedicated.Swyx [00:03:51]: Is there a best practice on when it's time to swap over?Philip [00:03:54]: Couple reasons. Yeah, reliability, that's a big one, right?Ali [00:03:57]: Like, if they have a very specific use case, they want you to train something specifically for them, like they want their own spec dec, for instance, for their own traffic.Swyx [00:04:04]: Spec dec is speculative decoding.Speculative Decoding and Custom SpeculatorsAli [00:04:05]: Speculative decoding, yeah.Swyx [00:04:07]: You have to explain.Ali [00:04:07]: Sorry. Like, speculative decoding is like, if you have a huge model, right? And so the model is going to be generating one token at a time every single turn, every single forward pass. So we attach, like, this little, like, parasite, like this layer that goes on top of the model, and this model just has to predict. It does three very fast autoregressive forward passes, and it will predict, like, three certain tokens, and then you do one forward stage over the entire original model in order to see if those predictions were correct or not, and then you accept them or you reject them. Now, this draft model is traffic specific, so if you, like, Philip said, if you're summarizing Harry Potter books, I can train exclusively that draft model on Harry Potter books, and I can guarantee you that I'm gonna accept the three tokens every single time. And so with that case, I increase your decode speed. I wouldn't be able to provide this to you if you're a shared endpointSwyx [00:04:53]: YeahAli [00:04:53]: ‘cause I have no idea if you're doing Harry Potter, if you're doing coding, if you're doing English. We don't know. Also, there was a thing in the book that mentioned that if they really cared about a specific threshold, chapter four, I think. Do you remember that?Philip [00:05:06]: Yeah. The things that you can do is you can set a specific, like, batch sizing, a specific, like, parallelism strategy if you're trying to optimize for, like, throughput versus latency. You can. Maybe a NVFP4 quant doesn't pass your benchmarks and you wanna run a model at higher precision, you could do that. There's just a bunch of reasons why you might wanna have your own endpoint and the biggest one, of course, just being, like, you don't have to deal with someone else doing a hundred million tokens of benchmarking traffic at the endpoint when you happen to be trying to serve your users.Swyx [00:05:40]: Yeah. I think one thing that is. That is a classic journey. Like, it's people is asking the, what happens when you type Google into the browser. Tool calling, is that just, you're generating JSON or is there more complication beyond that?Tool Calling, JSON, and Structured OutputsAli [00:05:58]: Certain customers that we have, they have their own post-trained models, and so they demand a tool calling that's not just, like parse a file or go find the weather. It's something that's very specific and you have to do post-training on this. And if the post-training on the model is not good or if the quantization after the post-training to get the inference to be fast, the model will struggle reading the JSON file and reading the tool calling. But it doesn't require its own like sandbox. It's not like it's going to use that tool calling to like escape a sandbox or like it doesn't have to be contained. It can just be a normal dedicated deployment. The challenge with tool calling more and more seems to be that the companies want certain tool calling which is a very sensitive thing to train. And because you're dealing with all of the JSON outputs, if it doesn't like close the end of the request in a very certain manner, you end up with a model that did the tool calling and like the thinking and so as a result of that, it didn't see the result and just hallucinated the result as it decoded. That seems to be the most challenging thing with tool calling, not really the sandboxes model.Philip [00:06:56]: Yeah, that's a challenge on the training side and then on the inference side, there's work that you can do to scope the possible output. So we published this at this point close to two years ago, the solution to this problem which is you make a state machine and you use that to constrain the output to a specific format. So this is the structured output problem. If you remember backSwyx [00:07:27]: Yeah, the specific grammar is,Philip [00:07:29]: Yeah, exactlySwyx [00:07:30]: GML had this thing.Philip [00:07:31]: Yeah. So it's like the old-school “make sure this is only JSON”, return only JSON orSwyx [00:07:38]: YeahPhilip [00:07:38]: Grandma's gonna die type of prompts.Swyx [00:07:39]: Is it BNF grammar? At some point OpenAI had released a thing that was like, yeah, if you want to constrain your output, write BNF grammar, back as NOR.Philip [00:07:47]: In our inference system, it's just a specified output format. And you get the guarantee that your output's gonna be structured along that format. And so applying that to tool calls can like help cut down on. You can still call the wrong tool or call no tool. It doesn't solve the certainty problem but it at least solves the output structuring problemSwyx [00:08:10]: YeahPhilip [00:08:10]: Within tool calls.Swyx [00:08:12]: And MCP is just another form of tool, right.Philip [00:08:14]: Yeah, exactly.Swyx [00:08:15]: As far as there's no special thing there.Philip [00:08:16]: The thing I'm always like explaining to people is the LLM is not capable of doing anything. It's only capable of making suggestions of what to do and then if those suggestions are formatted in a certain way and applied to a system that knows what to do with them, then an action occurs.Swyx [00:08:32]: Yeah. Part of the fun stuff is, this is solved outside of tool calling too. Like in an agent loop if the output is not correct or you're right, like reasoning, tool calling was done in the reasoning trace, just be like, “Oh, I don't know what to do. Let me just try again.” And it might get there after a few tries. And on your point of training, sometimes this is harder in smaller models, so you don't have the same exact quality outputAli [00:08:56]: Right.Swyx [00:08:57]: When you just swap from a big model, right?Ali [00:08:59]: Yeah. I will say that, before, I think we need to go back to inference engineering proper.Ali [00:09:04]: But, I had expected that something would replace JSON because it's hard to stream JSON ‘cause JSON must be complete and you must have open and close brackets and everything. So it's hard to parse something or validate something while it's being streamed. So people invented all sorts of things that are like, I forget the name of some of these alternatives, but it's something like TOML, something like YAML. But JSON seems to be dominant still.Philip [00:09:30]: The JSON outputs aren't that long, right? Like you could have a long-- ‘cause tool calls also contain the arguments in them and perhaps for a certain tool you might pass like a very long argument. But my impression of the median tool call is that it's a relatively small number of tokens, right? So I would expect that speculators are generally fairly good at something as formatted as JSON. And so you would have like a pretty fast decode step there and that the streaming wouldn't be as valuable, but maybe I'm wrong about that.Ali [00:10:02]: I think you're also bounded by the software or that the model is gonna integrate with if the software is built with JSON for the tool calls or if the company that you'- if your customer says that this is how our software works and our tools are interfaced with JSON, you can ask them to like, change their software and say like, “Yeah, this is gonna be better for the model.” but like with the right training shouldn't be that much of a difference. Also more profitable if it outputs more tokens probably.Swyx [00:10:25]: Depends on your business model.Swyx [00:10:27]: It really depends. But I will say that, as a writer with like experience a lot with generated output, I do try to move from text to JSON text which is very long JSON, right? Like there's paragraphs in every field because I'm trying to structure it, right?Philip [00:10:44]: Right.Swyx [00:10:44]: I want you to first make factual statements, then make opinions then make bullet point summaries, have dates, have entity references have your sources for references, all these things. Anyway, so these are things that like I think people who really experiment with structural output have to really care about. But, let's, let's recurse up the stack a little bit. Before we started recording, you mentioned something really cool, which is that there's a lot of engineering that-- inference engineering that goes on when a new model provider releases a new model, right? So let's call it GLM-5.2, Kimi K3. I had previously assumed, especially if it's like, well, GLM 5 to 5.1 to GLM-5.2, like that you've supported them before. Is it that much work?What It Takes to Support a New Open ModelAli [00:11:26]: It's a lot of work.Swyx [00:11:28]: Yeah. Okay. So like, a lot of people, all you guys, right whenever a new model launch like, people rush to say like, “Oh, Hugging Face supports this, Fireworks supports this, Spacetime supports this,” and I'm like, “Yeah, of course we support it.” But what goes into that? What goes intoPhilip [00:11:40]: I think it's more than just support it too, right? It benefits the consumer a lot. Like I think it was with Kimi K2.5 or GLM-5.2 the latest, there was an inference war, right? X provider is at 90 tokens a second. The next day we're at 150. The nextSwyx [00:11:55]: I kinda kicked that off with the GLM-5.2.Swyx [00:11:58]: I wrote a Twitter article about. It got like half a million views,Ali [00:12:02]: Based on being numberSwyx [00:12:03]: YeahAli [00:12:04]: Or it's for something else.Swyx [00:12:05]: Yeah. Which,Ali [00:12:06]: Oh my GodSwyx [00:12:07]: Which then got everyone really excited about, hey, how can we, bend tracks a little bit further and,Philip [00:12:14]: There's a difference between support the model, as in I can make a token out of this model, and support a model, as in I have a production-ready API from this model.Philip [00:12:26]: Getting to the point of I can make a token out of this model is not that hard because generally the, open source inference engines, vLLM, SGLang of the world oftentimes even receive weights ahead of time, maintainers do, or the people making the model merge PRs to ensure support. So you generally can, just get it working on the standard open source stack without too much pain in most cases. The challenge is, every inference company is gonna have own proprietary stack. Some open source components, some in-house stuff. And for any arbitrary model, there's going to be some new stuff. Sometimes you get lucky, like K, two five to two six was, like, pretty similar.Quantization, Speculators, and Production ReadinessAli [00:13:16]: Yeah. It was pure continued post-trainingPhilip [00:13:18]: YeahAli [00:13:18]: If I remember correctly.Philip [00:13:19]: Even in those cases, there's still stuff you have to do. You have to redo the quantization work. You're taking the model from. Generally, these models are not released in NVFP4, and we want them to be in NVFP4 for maximum Blackwell compatibility. So we have to perform that quantization, and, calibrate the quantization to make sure that we're not causing any regression in the model's intelligence. And then we also have to train the speculator, as we've talked about. Generally, we have. We have ZDR, zero data retention on our model APIs, so we don't know exactly the traffic that people are sending us, but we know what's popular. We know that coding use cases are popular. We know that agents, agentic use cases are popular. So we can get public data sets that are representative of that traffic and train general speculators. Now, with speculators today, you need to train the speculator using the base model itself because you're getting hidden states out of the model from running inference on these specific prompts, and that is the training data you use to create the speculator. So there's that process which you need the real model weights for. And then there's of course just the process of, standing up all the infrastructure behind it, loading all this stuff, testing it. And then when there's a new model with a newer architecture, I think that, like, the DeepSeek models tend to be the most challenging as they have, like, the most novel architectural stuff going on, model after model. But every new model has something. Kimi K2 had. Oh, sorry, GLM-5.2 hadAli [00:14:53]: Sparse attention.Philip [00:14:54]: Yeah,Ali [00:14:54]: YeahPhilip [00:14:54]: the DSA.Ali [00:14:55]: Right. Which is brought from DeepSeek.Philip [00:14:57]: Yeah. AndAli [00:14:59]: So you can copy-paste then?Philip [00:15:01]: It kindAli [00:15:01]: I don't know how this works.Philip [00:15:02]: So, like we had to, like, build support for that into our runtime. And you're right, like it is really interesting the way that all of these open source labs borrow from each other. For example, like GLM-5.2 doesn't have vision. So something that, Haley, a guy on our team, if we could take a look at this, he, like, grafted the Kimi vision encoder onto GLM-5.2.Retrofitting Vision into GLM-5.2Ali [00:15:27]: We'll be training the projector.Philip [00:15:28]: Exactly. So if you think about, like, the encoder, there's the encoder, which is the part that looks at the image and turns it into latent information, and then there's the projector which likeAli [00:15:38]: You can say latent space. It's okay.Philip [00:15:41]: And then there's the projector that maps it onto, the model itself, and then there's the model weights. You don't wanna mess with the model weights because you run a chance of making the model dumber at something else for the purpose of giving it vision. So instead, Haley started with just a projector, which is only a handful of millions of parameters.Ali [00:16:02]: That would be, yeah.Philip [00:16:02]: Yeah.Ali [00:16:03]: Can you show the training one?Ali [00:16:04]: Like the way it groksPhilip [00:16:05]: YeahAli [00:16:06]: Very interesting.Philip [00:16:06]: And maybeAli [00:16:07]: That right therePhilip [00:16:07]: Maybe Ali, you should take it from here. You've got a betterAli [00:16:10]: Ooh, double the sandPhilip [00:16:11]: Understanding of this than I do.Ali [00:16:11]: Yeah. You can see, like, he. The way he trained this is really cool. At the beginning, he was training it using just like, “Here's a picture of a mountain. Can you describe what's in this mountain?” And that caused it just like the first, learning walls. Like here you can see this all we're trying to teach it is to translate the encoded. Like it's already taken the encoder from Kimi K. It's taken the image. It'Philip [00:16:31]: Yeah. FrozenAli [00:16:31]: FrozenPhilip [00:16:32]: With adapter.Ali [00:16:32]: Exactly.Philip [00:16:33]: Yeah.Ali [00:16:33]: So the brain is frozen and the eyes are frozen. It's just we're tryingPhilip [00:16:37]: AlignAli [00:16:38]: Interconnect between the eye and the brain, right? So the projector. And so you take the tokens and then he's like, “Oh, can you describe what's in this image?” And he's like, “Oh, it's a mountain,” or it's a person or it's a human, whatever the case is. But that didn't cause complete understanding. So he changed it such that every image was associated with a data set of questions. Like, does this image have a white male? Does this image have birds in the top corner? Does this image have a scientist in it? All of that stuff. And it would have to answer questions correctly. And using not just training on describing an image, but being able to answer question, another question, answer over time. Like you can see the grokking, which is like genuinely insane, that retrofitting vision into a large LLM can learn to that extent. And even for images that it doesn't perform well on, for instance, if you ask it a picture of like Stephen Hawking, “Who is this?” Maybe it doesn't get it, but it will say something like, “This is Albert Einstein.” Like it still understandsPhilip [00:17:25]: Close enoughAli [00:17:26]: That this is a scientist who is a man who has, some significant achievements, all that stuff. So that's like really cool.Philip [00:17:32]: Yeah. So, we've covered Hao Tian before, who the author of the LLaVA paper that did this, a while ago. And I think that's very foundational work for anyone who hasn't done vision work before.Ali [00:17:41]: Same with the CLIP and MetaCLIP, where you go from just captioning to building out questionsPhilip [00:17:47]: RightAli [00:17:47]: Off the image and how much better you can get performance.Philip [00:17:50]: Right. Right. Right. Yeah. But what's, what's so exciting about this is if you look at a model like this. Now, this is a little bit more of a research project. It's not. It got to 56% on MMLU Pro, I think. So not quite frontier. But if you're running this model, you haven't suffered any loss on your GLM-5.2 quality. If you don't have an image, it'll just behave exactly the way it used to. And ultimatelyAli [00:18:14]: Which in the inference code you literally do not include the other part, right?Philip [00:18:18]: Yeah. You would just skip the encoder if you don't have an image input.Ali [00:18:22]: Okay.Philip [00:18:22]: Just confirming.Philip [00:18:23]: YeahAli [00:18:23]: Does it affect a lot on the overall inference side? Like you're not adding much, you're adding a very small vision encoder. These are typically likePhilip [00:18:30]: They're super fineAli [00:18:31]: Less than a billion parameters, right?Philip [00:18:32]: Yeah. It's, - There's a little bit less standardization among vision encodersSwyx [00:18:37]: YeahPhilip [00:18:37]: So the support matrix can be a little bit, sparser. But overall, yeah, it's a pretty, it's a pretty minor component of the overall system. And ultimately what you get out of the system is all of a sudden you have Kimi Vision, GLM weights, and DeepSeek attention all in one model.Open Source Model Grafting and Franken-MergesPhilip [00:18:56]: And that's, I think, a lot of the power and beauty of open source, is that you can take all of these different components and combine them together into a system that's better than anyoneSwyx [00:19:05]: YeahPhilip [00:19:05]: Can be individually.Swyx [00:19:06]: People used to say that you would also do Franken-merges where you would take likePhilip [00:19:10]: YeahSwyx [00:19:10]: Layers from each model.Swyx [00:19:11]: Does anyone do that anymore?Ali [00:19:13]: Well, to your point previously when you were mentioning like, the work that goes into supporting a model when it first comes out, like GLM-5.2 or MiniMax M3 or whatever the case is. Sometimes you do have to like, you do have to switch out some things. Like, for instance, the MiniMax M3 head uses full attention, and with full attention you end up with this like insane bottleneck in spec dec ‘cause you're doing auto-regressive token generation for three tokens, and you're doing this like N squared over all of the tokens that are in your sequence. Your KV cache is like very large because it's not sparse, it's not top K. So we find it better to like, okay, we're gonna replace this, we're gonna replace this layer with a layer from another model that's using like GQA, for instance. And then just with the right training, you can get it to have the same acceptance rate. So it is very possible to retrofit layers from other models and very much needed. If a layer is like inefficient, the training just becomes the challenge, like how do you ensure that you train it properly? Which again to your earlier point is like the mesh between training and inference. As in like you need very good training in order to do fast inference. That's like, I feel like more and more becoming true.Swyx [00:20:21]: Yeah. Anything else on the support side when you say like get it to fully production ready?Loop Detection, Race Conditions, and Non-DeterminismPhilip [00:20:26]: Yeah. I think that there's also a question of just, we can test a model to a pretty extensive degree, but we're trying to get it out quickly and then you see a bunch of other people test it and you get interesting results. There was an issue with, GLM briefly where we had some like mode collapses where it would just output the same token over and over again for certain prompts on certain temperatures. Like once you expose an endpoint to the real world, there's going to be, so many more varieties of things given to it that you're able to, discover and patch things. So it's not just a, day zero process, it's then like for the first week, for the first month, if a model remains popular, like how do you both fix bugs and then continue to push the envelope on performance?Ali [00:21:21]: What do you mean you don't want your model outputting S?Swyx [00:21:24]: Is there loop detection on that stuff, by the way? It still happens like quite a lot, which is surprising.Ali [00:21:30]: We have like we, in our endpoint, like if a model was to output the same token like four plus times, we just cut the generation. We say like, “Oh, sorry, this-- Like try again,” or like we will reprocess the request. ‘Cause we know then, like if it, like if, yeah, it's four times the same token, it's probably collapsed.Swyx [00:21:45]: Yeah. Is there a way to opt out in case I really want that?Ali [00:21:48]: You want that?Ali [00:21:50]: I think there's a way that we have to handle it. I'm not exactly certain, but I feel like in certain models, like when they output something like you can imagine, like a table for instance, and so they want, they wanna draw like 12 dashes and 12 dashes. Yeah, I think there's a way for that to happen. I think we only do it on certain tokens. Like we exclude certain special characters.Swyx [00:22:07]: Yeah.Ali [00:22:07]: So we only do it on like certain like S is the most common almost. GLM-5.2Swyx [00:22:11]: OhAli [00:22:11]: And I think it was DSV 4 as well. Like you'd just have like looping issues where like you literallySwyx [00:22:17]: ItAli [00:22:17]: Just have like S.Swyx [00:22:18]: Yeah. Is there a special, something special about S? No, just randomlyAli [00:22:21]: It just seems to be the one token involved.Swyx [00:22:23]: Yeah. And it'Philip [00:22:24]: Is thereSwyx [00:22:24]: And it's only temperature 0Ali [00:22:27]: NoSwyx [00:22:27]: Even at other temperaturesAli [00:22:27]: Even at like 0.9 or whatever, it will still, it will still collapse.Swyx [00:22:30]: That's weird, right?Ali [00:22:30]: It's, it is an inference problem to be honest, like a software problem. Like oftentimes, the image you run will-- like NVIDIA will release an image for instance, and if we will upstream the changes from their latest TensorRT-LLM image into our stack, we'll find that it fixes it. Or oftentimes this will only happen in an inference engine that you're using like SGLang. But if you were to switch to vLLM, that isn't the case. So it seems to be like an extremely like deterministic software issue and not really a model issue. It's not like a weights problem. Like I'- we'll say like, “Oh, it's a problem with the quant. We did PTQ wrong,” right? But that isn't, that doesn't make sense because the same weights used with a different inference engine does not repeat the problem. And sometimes it's, the kernels that are being used in the backend have like these very subtle sometimes race conditions, where if you were to use this model hosted on one cluster, you will never get this problem.Swyx [00:23:19]: Oh my God.Ali [00:23:19]: But if you host it on a different cluster, you will. And the reason is the KV cache transfer from a node to node in that one cluster is using a slower interconnect than the node to node in another cluster. So that exposes the race, whereas in another cluster it doesn't. So then you end up just like, okay, this model is not gonna be hosted on this cluster. We're gonna host it on, another cluster because that cluster exposed that problem. But then it ends up with like, okay, is it the software? Is it the model weights or is it the hardware?Swyx [00:23:42]: There is a thing about this with temperature 0 still not being deterministic, right?Ali [00:23:46]: Right.Swyx [00:23:46]: Mostly because of hardware. Even at temperature 0 same model, you won't always get the same output.Swyx [00:23:52]: Even-- But I'm surprised by the race condition one because, I thought PyTorch was a graph that like guarantees that you at least, execute things in the right order.Ali [00:24:02]: Well, yeah, true. Like I'm not, I'm not saying that there is. Like well, you have things like PTL optimizations where like you can start a kernel before the end of the previous kernel, and that's like ‘cause you want to do that because there'sSwyx [00:24:12]: It's like pipeliningAli [00:24:12]: Expense. Exactly.Swyx [00:24:13]: Yeah.Ali [00:24:13]: But it'- But you don't do it cleanly. Like you overlap a little bit of the execution. No, it is very possible that the kernel itself, like that one block that is supposed to be running in this instance of time, that kernel itself has a race condition. For instance, like a missing barrier. Like often if you're designing a kernel and you want it to make it to be very fast, if you don't test it extensively, you'll, you'll have certain threads access data points from registers before they've been written to by other threadsSwyx [00:24:36]: YeahAli [00:24:36]: For example, because like your barrier is wrong or your synchronization was wrong. But yeah, like the testing itself is very difficult in those like, andSwyx [00:24:42]: And there's no like borrow checkerAli [00:24:45]: What does that mean?Swyx [00:24:46]: Like Rust. Like the. If you're trying to have like memory safety It sounds like a comparable problem.Ali [00:24:52]: Well, yes, but you're working in CUDA, right, NVIDIA GPUs. Like- You just need a higher level language like modular Maybe that's what modular is supposed to do. I don't know.Quantization Quality and Vendor FidelityVibhu [00:25:00]: How do you see keeping quality of the model? So you talked about all these steps of, okay, you gotta do quantization, train your own speculative decoderAli [00:25:07]: RightVibhu [00:25:07]: Run on different hardware. Looking at other model providers, okay, you kicked off a inference speed race on the consumer end. What goes into keeping quality the same across them, right? Sure, you can run benchmarksAli [00:25:22]: YeahVibhu [00:25:22]: But, like, how do you determine how much quantization are there standards? What goes intoPhilip [00:25:27]: There's a few things on quality. Most inference optimizations are lossless. KV caching, for example. You are just recomputing or preventing recomputing the same values. Speculation, of course, if a draft token is wrong, it gets rejected. The main lossy optimization is quantization. And that really comes down to, number one, data format, number two, which parts of the model you choose to quantize, which layers, and number three, like doing a lot of calibration on the quantized weights, to ensure that you're preserving all the outliers. There's other tricks that you can do, though. A big one is long context, ‘cause one thing you asked at, right at the beginning is, “Oh, what's gonna happen if I send a 200,000 token request in?” So with a long input sequence, you need to, store a lot more information. You need to process a lot more tokens. And so even if a model has a context of a certain length, you might, as an inference provider, choose to build an API with a shorter context length, and of course a full length one as well. Because if someone doesn't need the full million token context, for example, you can get them better performance. I don't know if that's exactly like quality of the model. The way that I think about quality is to what degree are we faithfully serving the original model? If you think of a golden implementation of a model that performs exactly the way the model is designed to perform, I think of quality as how close are we getting to that, 100% fidelity of the model.Philip [00:27:13]: You can also, of course, think about quality from the training side and how do you push yourself past 100%. But when I think about purely inference optimizations, it's getting faster while staying as close to that 100% fidelity mark as possible. And certainly our standard internally is that, like you should not be able to tell the difference between our API and a, official API. I think Kimi in particular does a good job of vendor benchmarking hereAli [00:27:41]: YesPhilip [00:27:41]: Where they haveAli [00:27:42]: They released an actual vendor benchmark.Philip [00:27:43]: Exactly, yeah.Ali [00:27:44]: ‘Cause they accused, some people, Amazon? There was some provider that was not doing very well on Kimi's benchmark.Philip [00:27:50]: Yeah.Philip [00:27:51]: So, with Reflect we probablyVibhu [00:27:52]: This was a long time ago, right?Philip [00:27:54]: No.Ali [00:27:54]: Yeah, like threeVibhu [00:27:55]: They alsoAli [00:27:55]: Four, five months agoVibhu [00:27:57]: This also happened with, I don't remember which model, but they pulled out quite a few, and then they started a whole chart about this. It might have beenPhilip [00:28:03]: Kimi Vendor Verifier.Ali [00:28:04]: Yeah.Philip [00:28:05]: Yeah.Ali [00:28:05]: Yeah, ‘cause you, ‘cause you'd be pissed, right? Like if you'Philip [00:28:07]: Yeah.Ali [00:28:07]: If like if I'm a consumer and I'm using like Amazon's endpoint for instance, and I've used Kimi and I'm like, “Oh my God, like this is bad,” I'm not gonna say, “Oh, Amazon quantized the model in a bad way.” I'm gonna say, “Oh, Kimi sucks.” Right?Philip [00:28:17]: Yeah.Ali [00:28:17]: So it seems like that makes sense.Philip [00:28:19]: Yeah, they care. They care.Vibhu [00:28:21]: Justifiably.Ali [00:28:21]: Yeah, justifiably.Vibhu [00:28:22]: This is probably a stupid question, but just checking, has anything improved from main quantization?Philip [00:28:28]: Yeah.Vibhu [00:28:28]: Like, is quantization always strictly worse?Ali [00:28:30]: Well technicallyVibhu [00:28:32]: NoAli [00:28:32]: It's a lossy. QuantizationPhilip [00:28:33]: YeahAli [00:28:33]: Is a lossy, it's a lossy implementation.Philip [00:28:36]: Speed improvesVibhu [00:28:36]: Speed improves.Ali [00:28:37]: It the number, likeVibhu [00:28:38]: No, I' always look for inverse scaling laws.Philip [00:28:40]: Yeah.Ali [00:28:40]: Yeah.Vibhu [00:28:40]: This is something I learned from Noam Brown, where like things that normally act in one direction sometimes do.Philip [00:28:45]: Well, technically when you run a benchmark, because these models are deterministic, sometimes your,Ali [00:28:52]: YeahPhilip [00:28:52]: NVFP4 quant is like, two basis points higher than yourAli [00:28:56]: No, it's noise. It's noise.Philip [00:28:57]: Yeah, exactly. I'm like, yeah, it's, it's within. That's why I always say within margin of error.Philip [00:29:01]: And I stopped saying that because everyone assumes that what is, well, within some margin of error, we're barely inside of that to the worst, so we're saying. But yeah, sometimes it's just like, gives you a higher output score. But like Ali said, that's noise. To my knowledge, you're not necessarily making the results better. You're just trying to, again, like keep your fidelity as close to 100% to the original model.Layer Selection, KL Divergence, and Better QuantizationAli [00:29:27]: There is, to your point, research that we did on MP. I don't know if you are able to pullPhilip [00:29:31]: YeahAli [00:29:32]: A tweet we did. One of our research interns, Joshua, I think it's a tweet on how we have 20% better quantized GLM-5.2 than NVIDIA. Essentially what we found throughout like this month research is, okay, quantization is a lossy. It's. You're compressing the data from, occupying 16 bits to occupying, four bits, for instance. And so you're losing some information, and you're trying to minimize that. And so when I say that I'm gonna quantize the model, my job becomes how do I find the layers that I can quantize, and how to find the layers to not. For instance, with image models, I don't quantize modulation layers, and I don't quantize out projections because those two are. Like out projection is what you see as the user. Modulation is what the model sees or understands. Right, exactly. And so to his paper, do you have the. It doesn't have the. Yeah. It's a long paper. I don't know if I can findVibhu [00:30:25]: If there's a part to search or it's probably in the thread.Ali [00:30:28]: It's probably in the thread.Vibhu [00:30:29]: Yeah.Ali [00:30:29]: But the long and the short is it is very possible that quantizing more of the model makes the results. Like if I have a model that I quantize layers one, five, and 10, and another model where I only quantize layers one and It is possible that the model in which I quantized more information is going to perform better because the quantization errors have canceled out. And so what Joshua showed in his mathematical proof where he had like a verifier in, is that you can predict which layers are going to have quantization errors that will cancel out with each other, and you choose to quantize those layers. And so the result of doing this mathematical quantization is you end up with a model that's 20% more quantized than another provider, so you get 20% more throughput of it because there's more layers than running an NVFP4, and your quality is better than that other quant because the layers that you chose to quantize have their errors cancel out, like one layer skewed to the right one layer skewed to the left, one layer skewed to the right. Your final logits distribution is more similar to the original distribution of the model, so you have better fidelity. And so the way we proved this was with KL divergence. So instead of just scoring on the benchmarks, we scored the KL divergence between the logit distribution of the quantized model and the logit distribution of the original full precision model, and we showed that with this technique we get. If your probability distribution on the logits which token it wants to select is more of the same as the original model, you're probably gonna end up staying true to the original model. So yeah, so it seems like previously before this, it seemed like the industry was, well, the more you quantize, the worse it's gonna be, ‘cause the more loss you introduce. That's not exactly, not necessarily true. So yeah, doesn't improve it, but can cancel out.Philip [00:31:57]: I think it might be this, but reminds me a good bit about pruning where you can prune off certain layers.Philip [00:32:03]: But very interesting. Didn't know this was a whole paper you guys put out.Ali [00:32:06]: It's. Fun fact, it was originally 72 pages, this paper, and then we decidedPhilip [00:32:11]: WowAli [00:32:11]: We can't tell. We couldn't release it. So it's now 45.Swyx [00:32:15]: Still 39 pages, so very substantive. We talked about evals and all these things and, like what's possible in terms of speedup? Like it's like probably like the numberInference Speedups and BenchmarkingSwyx [00:32:25]: Thing that people do wanna care about, and it's something that you wrote about in your post. Like official API is 70 tokens per second, and you push it up to 90. Is that like a normal thing?Philip [00:32:36]: So what's cool about working in inference, the reason that I think inference is going to be a useful place to do engineering for a long time, is that if you look at highly optimized domains like, say, finance, if you're in finance, you measure how much better you got in basis points. It's like, “Oh, I got five basis points better, like twentieth of 1% better,” that's huge news because everything is so optimized. When we publish optimizations, it's 20%, it's 100% it's 200%. So there's still probably like a lot further to go, honestly. Like you'll, you'll know that inference is pretty much solved when researchers start publishing about how they got 1% faster at something.Swyx [00:33:19]: Which by the way, because I am from the finance background, in the ‘70s, that was the margin at the time. When you did quantitative finance research, you would findAli [00:33:27]: And like 20%, tens of percent.Swyx [00:33:29]: That's. Yes.Philip [00:33:29]: Yeah.Swyx [00:33:30]: And now it'Philip [00:33:31]: Tiny fractionsSwyx [00:33:32]: For those people interested, look up Andrew Lo's paper. He had a really interesting illustration of quant, stat arb, distribution, narrowing down from like those kinds of 20% differences in the ‘70s, down to nothing today, which is very cool.Philip [00:33:48]: Exactly, and we're at the beginning of the same type of thing. Now benchmarking is hard. I think anyone will tell you that, and benchmarking provider speeds is hard because there's so many variables that go into it. What hardware are you using? How much load do you have on the system? What's the exact nature of the prompts and input and output sequence lengths? All that stuff. But overall, when you start stacking these improvements, you're looking at multiples. You can look at it. The most common form, of course, is TPS, tokens per second, which is bad naming by us in the industry, ‘cause there's two tokens per second. There's tokens per second, the throughput number, and the latency number.Ali [00:34:31]: TTMT, yeah.Philip [00:34:32]: Like total tokens per second out of the, out of the GPU as a throughput number. Most people only care about tokens per second as the latency number, which we should call ITL, intertoken latency, but we don't.Philip [00:34:44]: Anyway, so you can imagine a standard API without many optimizations for a 1 trillion parameter model operating somewhere in the 30 to 50 tokens per second range for reasonable traffic profile. And we generally see the goal of, pushing to 10X that. But, not necessarily day zero, but by stacking enough optimizations, if you have, say like four optimizations, each of which doubles performance. Or sorry, three optimizations, each of which doubles performance, then you stack that up, that's an 8X gain. That's the order of magnitude that we're working with in this space. We're trying to make things substantially faster, not just go from like 70 to 90.Swyx [00:35:38]: Are you saying you've. You have done that?Philip [00:35:40]: So let's say you have as a reasonable baseline, 30 or 40 tokens per second. You can achieve 10X that. So like on GLM-5.2, if you run it unquantized, perhaps on H100s even, and you're just using an off-the-shelf inference engine with no particular optimizations, no speculator, nothing extra around like KV routing, no disaggregation, you're, you're probably, yeah, looking at that like 30 to 40. You think that's like a reasonable baseline?Swyx [00:36:12]: Right. Right.Philip [00:36:12]: To get to something like 10X, there's a lot of trade-offs that you're making. If we're running at more like a 300, 400 tokens per second range, you are using the best hardware possible. You have a optimized speculator. You have done all of your quantization work. You are Seeing a pretty high cache hit rate. You are running with a reasonably small batch size and a parallelism configuration that is tuned for latency versus throughput, but it is possible. So the spreads that you see if you, like, go on artificial analysis or you go on OpenRouter and you look at, the worst provider to the best provider, oftentimes can hit that range. 10X is of course very aggressive. It's oftentimes maybe more of a four to six times improvement. But that's the performance that makes us really excited, is when we can get these huge gains, not just go from 70 to 90 tokens.Stacking Optimizations: NVFP4, Speculation, and DisaggregationAli [00:37:19]: It's also, like, hardware dependent. Like, ifPhilip [00:37:20]: YeahAli [00:37:20]: If you have a thing where you're serving it on just, like, a node of H100s and then you throw, like, you shard the model across, like, four nodes of B200s. Like, you can definitely increase the speed with just throwing more hardware at it. Like, normalizing for the same exact hardware and the same number of GPUs.Philip [00:37:35]: Yeah. Then you're looking at, like, a two to 4X improvementAli [00:37:38]: Right. RightPhilip [00:37:38]: Depending on the inference optimizations. So yeah, it's. Some of it's, what's the call, and some of it's who's the driver.Vibhu [00:37:46]: If you break down the two to 4X, say the example is run GLM-5.2Ali [00:37:51]: YeahVibhu [00:37:51]: On B200sAli [00:37:53]: YeahVibhu [00:37:53]: Single node, right? What's, like, the cost trade-off for effort to get, like, the last bit of juice out versus what should people just think of, right?Ali [00:38:01]: Spectre quantization. Yeah.Vibhu [00:38:03]: Spectre quantization.Ali [00:38:04]: That's, that's, that's like 95%. LikeVibhu [00:38:06]: And how far does that get you? And how easy is that for the average person to do? So say right I wanna throw the weights of GLM-5.2 on a node of B200s, how easy is it to find speculative decoder- decoder model or already quantized model? How much work goes into it?Philip [00:38:23]: If you're doing it up front, it's quite a lot of work. If you're doing it today, there's going to be people who have published things that you can just, you can just grab some NVFP4 weights. You can grab a speculator. Yeah, if we're thinking about, like, what are the 2Xs we're stacking, going from, BF16 to NVFP4 is, it's not quite a 2X, right? It's like. I think it's about, like, 30 to 40%, from 16 to 8, and then another 30 to 40% multiplied from, 8 to 4. So that doesn't quite get you a 2X, but, like, roughly a 2X. Speculator, roughly a 2X. Disagg on top of that if you're able to get enough hardware and put enough traffic through it, another roughly a 2X. And then you add in some, double-digit percent increase from having just a better runtime with, the latest kernels and stuff behind it. And that's how it stacks up.Ali [00:39:21]: YeahPhilip [00:39:21]: So building each of those, like, building the, quantized weights is, for someone who really knows what they're doing, hours to days of work. Building the speculator, again, like, hours to days of work. And the, disagg setup, hours to days. Well okay, but like once you haveAli [00:39:39]: Once set up. Once set up. YeahPhilip [00:39:40]: Yeah, getting disagg working for the first time, I'm saying, of course, is very difficult.Philip [00:39:44]: The marginal implementationAli [00:39:48]: Like, if you're just grabbing, like if you are a person, like just a normal consumer who has access to, like, a node of B200s and you're wondering, “How can I just host it myself?” You don't need to quantize the model yourself. There's always gonna be, like, an open source quantized checkpoint. NVIDIA's gonna push one out if no one else does. You. Usually, the providers will have their own spec dec that they've trained as well. You don't need to train your own spec dec. You can just use that as well.Philip [00:40:09]: Yeah. Like, GLM-5.2 has its own MTP.Ali [00:40:13]: Right. Right.Vibhu [00:40:14]: What's multi token prediction?Philip [00:40:15]: Yes.Ali [00:40:16]: I'm justVibhu [00:40:16]: Can you explain that?Ali [00:40:16]: I'm just an expert.Ali [00:40:18]: I can do it for you in case I get it wrong?Vibhu [00:40:20]: No.Vibhu [00:40:21]: Yeah, you should correct if we're wrong, but their multi-token prediction can be used for self-speculative decoding.Ali [00:40:27]: I'm not sure. I'm not gonna correct that.Vibhu [00:40:28]: Okay. I'm semi-confident in thatAli [00:40:30]: Okay. YeahVibhu [00:40:30]: But someone can check. But it's useful to paint the story of, okay, not just the average person, but say a company wants to switch from serverless inference I wanna throw this up on. I wanna rent some GPUs, throw it up. These are the steps you take to do significantly faster than just put it behind vLLM.Ali [00:40:48]: Right.Vibhu [00:40:49]: I was waiting for a mention of Dynamo.Vibhu [00:40:51]: I feel like, that's supposed to be the baseline that you measure against.Dynamo, KV Routing, and Disaggregation ToolkitsPhilip [00:40:55]: I would think of Dynamo as less of a box system and more of a toolkit for building with. So when we talk about doing aware routing, when we talk about doing KV offloading, when we talk about doing, PD disaggregation, Dynamo fundamentally is. By the way, Dynamo is an open source library from NVIDIA.Ali [00:41:17]: We've done a pod with KylePhilip [00:41:18]: OkayAli [00:41:19]: Kyle Cranin.Philip [00:41:19]: Cool. So then your listeners know then that it supports all the different inference frameworks. And it is multi hardware, which is interesting.Ali [00:41:28]: But it's just a router, it's not like an optimizer layer.Philip [00:41:30]: Yeah. All it does, like, what Dynamo is good at, it is a library for moving information around your cluster, around your hardware. So if you have, KV cache on one place and you need it to be somewhere else, Dynamo coordinates NIXL for you to move that around.Philip [00:41:49]: That doesn't mean that, like, out of the box, you just say, “Pip install Dynamo,” and then you get, like, a massive performance speed up. It's more of a developer toolkit.Ali [00:42:01]: Yeah. I would have said it would. It comes with a set of defaults that you can then swap out.Philip [00:42:06]: It does. If the industry at large, I think, was, like, rolling out all of these deployments, standard, then I think it would be, like, a credible baseline. But, we've got to, we've got to benchmark against, like, what we're seeing in the wild.Speculative Decoding Methods: Medusa, EAGLE, n-Gram, and Spec-SpecVibhu [00:42:23]: I did wanna talk a little bit more about PD disagg, because that is probably, like, number three after quantized and speculative decoding. In your book though, I was just gonna pull out the book.Philip [00:42:31]: Yeah.Vibhu [00:42:32]: Like section 522 on Medusa, 523 on EAGLEPhilip [00:42:35]: YeahVibhu [00:42:36]: 524 on gram.Philip [00:42:37]: It's 55, would be disaggregationAli [00:42:42]: Yeah. Well, no, I just wanted to dwell a little bitPhilip [00:42:44]: YeahAli [00:42:44]: The other. Like, so what do you choose to include? What do you choose to not to include? Because there was all these other techniques.Philip [00:42:51]: Yeah.Ali [00:42:51]: Are these still relevant? Because I think they came out, like, a year and a half ago maybe.Vibhu [00:42:55]: Medusa is quite old.Philip [00:42:56]: Yeah, Medusa's old.Ali [00:42:58]: It was old.Vibhu [00:42:58]: But is it in the book as a good, here'sPhilip [00:43:01]: BaselineVibhu [00:43:01]: Baseline vanilla understand it?Philip [00:43:02]: Like you should know this.Vibhu [00:43:03]: Like I read the paper, I'm like, “ it makes so much sense.”Philip [00:43:05]: Yeah.Philip [00:43:05]: So with the book, I had a couple goals. One was to give people just a working vocabulary for the space as a whole, and the other was to give them some intuition about how each of these techniques works. As I mentioned in my AI Engineer talk, which is the first public addendum to this, the speculation space has moved much faster than everything else. So yeah, even at the time that I wrote the book Medusa, I very much included as a way for people to understand how the space evolved rather than what the most modern technique is. And now of course, there's DFlash, dSpark. There's, there's newer techniques even than EAGLE, although EAGLE is still very commonly used.Ali [00:43:51]: SpecSpecta.Philip [00:43:52]: Yes. Speculative decoding.Vibhu [00:43:54]: What canAli [00:43:56]: Oh, it's a paper by Tri Dao and it's like, it's doing speculative decodingVibhu [00:44:00]: HuhAli [00:44:01]: For the speculative decoder.Philip [00:44:02]: Oh, in spec- oh my God.Ali [00:44:02]: It's literally just an another. It's like, yeah, that's the most simple way to explain it, and it seems like he got trivial speed ups there. But it seems that the complexity with training, it's almost like in our mind at least, it's almost as complex as training GANs. Like it's like a very delicate balance and oftentimes you, it's just but yeah, it's literally speculative decoding on speculative decoding.Vibhu [00:44:21]: Speculative.Ali [00:44:22]: Yeah. We saw this paper.Vibhu [00:44:24]: It's interesting, right?Ali [00:44:24]: Yeah.Vibhu [00:44:24]: I wouldn't even expect it to be very particular to train, I wouldAli [00:44:29]: Right.Vibhu [00:44:29]: The naive part of me is like, okay, train speculative decoder.Ali [00:44:32]: But like, and it makes sense, like the whole idea of speculative decoding is you. It's like, it's like almost like the iPhone auto predict version but for a normal model, right? Like you're just, you're just, generating three tokens and you're like, okay, I'll do prefill on them. And so you save those three turns for your original model. Now your speculative decoder is doing three turns of auto regression, so why not just have an even smaller model?Ali [00:44:53]: The other question there is what are the size of speculators? So say forPhilip [00:44:58]: Right. It's like a billion parameters.Ali [00:45:01]: Like for MiniMax, it's. Yeah. It's like one layer. It's like one 60th of the original model usually.Philip [00:45:06]: Yeah. I think we should do a paper when we get back to the office.Philip [00:45:10]: SpeculativeAli [00:45:11]: SpeculativePhilip [00:45:11]: Decoding.Ali [00:45:13]: No, it's, it does seem like how, when do you stop? But then it also seems like if you're able to train spec-spec decode for instance, right? Like if you're able to have a small model that is accurately predicts what the intermediate speculator is gonna predict, that is able to predict what the original target model's gonna predict, then why not just use that smallest model directly, right?Vibhu [00:45:34]: Yeah. This isAli [00:45:35]: Like it seems likeVibhu [00:45:35]: Adjacent to the routing problem.Ali [00:45:36]: Right.Vibhu [00:45:36]: Yeah.Ali [00:45:36]: Right.Philip [00:45:37]: The thing with speculators is one of the practical constraints on using them is that you do have to run a small model on the same hardware that you're running the big model on. There is a orchestration and resource competition problem inherent in that, and that is one of the constraints on speculation in general, is that draft tokens cost resources to create and cost software complexity to manage. And so if you have like infinitely recursive speculators, you add in quite a bit of that complexity on the actual implementation within the inference engine as well, not just in the training process.Vibhu [00:46:17]: I was gonna say, I would wonder if you could do similar, like distillation and pruning of, it's the same thing, it's just a model. Can we not just distill a lot of the weights, quantize the speculator, out of my domain? The question that also comes up is, this is all for big server workloads, right? How much of this applies to, say I have this MacBook, I wanna run Gemma really efficiently. Similar problems, not the same?Local AI vs. Data Center InferencePhilip [00:46:45]: Pretty different. I talked to Selo, about this on his podcast a couple weeks ago. The difference between inference engineering for the data center and for production workloads versus inference engineering for local AI, is that we start with fundamentally like different constraints and different goals. With local AI, it's how do I fit this model onto my hardware and then make it less dumb? And with data center influence, it's how do I load this model and then make it less slow? And we care about less dumb, and they care about less slow. But the local AI inference engineering ecosystem, I think has a lot for us to learn from in the data center space. They are experts in various forms of quantization, including dynamic quantization that we just don't touch, in the pruning, in the distillation, in the, layer removal. There'Ali [00:47:42]: Layer removal matters less.Philip [00:47:43]: Yeah. There'Ali [00:47:44]: No one loves pruning really.Philip [00:47:45]: Yeah. Well, but the, but they doVibhu [00:47:46]: Which is surprising, right? But that's, that's a whole different thingPhilip [00:47:48]: Just to fit something on the laptop.Ali [00:47:50]: Right.Philip [00:47:50]: So yeah, it's a, it's an interesting, it's an interesting space. Not necessarily that like their techniques make sense for us to do in the data center, because we have different resources and different goals, but more that the process as well as the openness of that field is something to, admire.Ali [00:48:12]: Yeah. Like to your point, like, certain optimizations that would. Like for instance, Turbo Quantum Sharper, like it made such huge hype on that and we did like a whole deep dive on Twitter and like said, what is it? How does it work? Why is it good or not? And it took off and it was implemented on local devices because your memory bandwidth is so slow on like a MacBook, for instance. But try putting the same thing on like an NVIDIA GPU on a B200 Turbo quant would not be. Like, it would not be used. Like, NVIDIA - Like, NVIDIA made it clear that this is not a good optimization, and we've seen it firsthand where the overhead of doing dequantization, quantization of, in the kernel itself with turbo quant kernel, each end is much slower than the time that you save from doing the bandwidth. ‘Cause on the B200s, you have like 3.5 terabytes per second. You don't need decrease the storage that much. You don't need to do, FP4 KV cache. You don't need to use a requant. There's, there's, there's better optimizations to be made. But on Edge devices, it's extremely important, it's extremely useful. So, seems to be, like, different optimizations there, but then they're all uniquely combined with like all you wanna quantize the model, you wanna do speculative decoding, like certain common prefixes with bothPhilip [00:49:18]: Principles.Ali [00:49:19]: Yeah, exactly. Exactly. Exactly.Philip [00:49:20]: They also do a lot of work on, model parallelism, especially over, heterogeneous topology, where you have, some sparks and they are wired together with, Ethernet, DGX sparks.Ali [00:49:35]: Yeah, this is the Exo Labs guys.Philip [00:49:36]: Yeah. You have, a nu
Head to https://buyraycon.com/friendsOPEN to get up to 20% off Raycon audio products. Thanks to Raycon for sponsoring FPS! -- Timestamps: 00:00 Intro 07:22 Ralph and Jake have been playing Halo Campaign Evolved 45:17 Raycon (ad) 48:38 Lucy's been playing Silent Hill: Townfall 01:04:26 Ralph's been playing Splatoon Raiders 01:14:57 Jake's been playing Rhythm Heaven Groove 01:16:58 User Question 01:31:10 Lucy's been playing Clicky Islands, The House Dreams Along With Them, and The Incident at Galley House 01:36:43 Show and Tell 01:47:25 Wrap Up -- Listen to the Friends Per Second Podcast on your favourite podcast platform: https://linktr.ee/friendspersecond Follow on Instagram: https://www.instagram.com/friendspersecond -- Let's meet our hosts! - Jake Baldino (aka the Before You Buy Guy) is pretty much the most watched reviewer on YouTube across both Gameranx and his personal channel (https://www.youtube.com/c/JakeBaldino). If you're obsessed with Delorians, The Mummy and Pizza you can discuss that stuff with him directly over on Twitter: @JakeBaldino - Lucy James was a Senior Producer at Gamespot for more than a decade before striking out on her own, founding https://lookingfor.game/, a newsletter and games discovery platform that serves up cool suggestions (and demo codes) directly to your inbox. She also co-hosts the annual Summer Games Showcase alongside Geoff Keighly. - Skill Up used to work at McDonalds but he got fired for skimming too many chicken nuggets. He says he regrets it since he hasn't had a better job since. Learn more about your ad choices. Visit megaphone.fm/adchoices
In this episode, Conor and Ben chat about networking in FPS vs RTS video games, the state of the video game industry and more!Link to Episode 297 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)SocialsADSP: The Podcast: TwitterConor Hoekstra: LinkTree / BioBen Deane: Twitter | BlueSkyShow NotesDate Recorded: 2026-07-22Date Released: 2026-07-31Real-time strategy (RTS)First-person shooter (FPS)The Legend of Zelda: Tears of the KingdomReturn of the Obra DinnIntro Song InfoMiss You by Sarah Jansen https://soundcloud.com/sarahjansenmusicCreative Commons — Attribution 3.0 Unported — CC BY 3.0Free Download / Stream: http://bit.ly/l-miss-youMusic promoted by Audio Library https://youtu.be/iYYxnasvfx8
In this episode we discuss the late Sam Neill and some of our favorites—Event Horizon, In the Mouth of Madness, Dead Calm, and Tales from the Crypt: Demon Knight (yeah we know its not a Sam Neill, but you will get it)—before jumping into Falling Skies (fun show, but the gun handling and mixed-mag squads leave something to be desired) and the eternal “still good to some, trash to others” debate around The Walking Dead. We also check out Undead Chronicles, the zombie FPS with slow-moving undead that's currently soliciting feedback on its latest visuals. More B&T GHM9 (Hunter loves it when I bring this up), B&T FRT is installed runs well! I received in some bullets, 147-grain 9mm and 158-grain .38 Special ammo. Just gearing up to load more subsonic ammo. With vacation approaching we talk defense/escape gun choices and the eternal struggle of wanting to shoot at the beach… only to settle for dry-fire practice. Where to find us: Livefire-media.com Rangehot.com Social Links: IG - @livefirem - @rangehot.com_offical X - @LiveFireM - @rangehotdotcom FB - Live Fire Media - Range Hot Live Fire Media on RSS.com
Andrew Hulshult has become one of the defining composers of modern FPS games, with credits including DOOM Eternal: The Ancient Gods, DOOM + DOOM II, DUSK, Prodeus, Heretic + Hexen, Quake Champions, and more. In this exclusive interview, Andrew joins The Expansion Pass to discuss his journey from learning guitar through Napster to becoming one of gaming's most recognizable composers. We explore how the fan-favorite IDKFA soundtrack became official DOOM content, the pressure of following legendary composers Bobby Prince and Mick Gordon, and the incredible story behind choosing to score Iron Lung instead of pursuing what became DOOM: The Dark Ages. We also discuss: How Andrew got started in music The origins of IDKFA Working with id Software DOOM Eternal: The Ancient Gods DUSK and the rise of New Blood Interactive Steam Deck and favorite games Composing under impossible deadlines Indie vs. AAA development Dream franchises he'd still love to score Upcoming projects Whether you're a fan of DOOM, retro FPS games, game music, or simply enjoy hearing from the developers shaping the industry, this is a conversation you won't want to miss. Thank you for listening to The Expansion Pass. If you enjoyed the interview, please consider following the show and leaving a review—it helps us continue bringing conversations like this to the community.
WARDOGS ran its first ever gameplay livestream this week and it went about as well as you'd expect from a studio full of guys in their late twenties: the alpha codes were a QR-code disaster, the stream started two hours late, and it ended with a Twitch ban for throwing a sex toy across the room. And yet — the actual gameplay looked incredible. We break down everything we saw: the enormous map, the three-team control zone loop, the destructible environments, the buy menu, and why Bulkhead's 60 FPS performance floor and "gamers first, devs second" attitude has us more hyped for this game than anything else on the calendar. Plus: is a monthly subscription for better servers and anti-cheat a genuinely good idea or the start of a slippery slope? Then it's Hell Let Loose: Vietnam, which just opened its first free crossplay playtest on the new Đắk Tô Airfield map — the performance is fixed, the hit reg is fixed, and the map is a massive upgrade over that awful jungle one. And Battlefield 6 Season 4 finally brought naval warfare, Tsuru Reef, and a Top Gun crossover that people are somehow mad about. We close with what we've actually been playing — Overwatch ranked, Stellaris Nomads, a lot of Tarkov, and one MMO Raz will absolutely keep playing despite telling you not to touch it. Jake's out here getting one-tapped in Tarkov. Raz is defending a game he just called a pile of dog shit. Business as usual. 0:00 - Intro 1:40 - Patreon + Welcome To The New Gold Patrons 3:21 - Patreon Hangouts Are Back In August 4:40 - WARDOGS Ran Its First Gameplay Livestream 5:42 - QR Code Chaos, A Flying Dildo, And A Twitch Ban 6:52 - The Gameplay Though? Actually Incredible 10:56 - This Map Might Be Too Big 13:37 - Why A Huge Map Makes Your Life Matter 15:24 - The Buy Menu vs. Tarkov's Nightmare Menu 16:14 - WARDOGS Isn't Really A New Genre 18:38 - Flight Of The Valkyries In A Helicopter 19:35 - Three Teams, Base Building, And What We Didn't See 21:46 - The Devs Are Just A Bunch Of Bros (And That's Good) 23:58 - Shock Collars And Shots At Hasan 25:27 - The Performance Dev Vlog: 60 FPS Is The Floor 29:44 - Gamers First, Devs Second 30:39 - 650,000 Wishlists vs. 5,000 Expected Players 36:28 - Joe Brammer's Monthly Subscription Idea 41:39 - What Will WARDOGS Actually Cost? 46:55 - Reddit Hates The Subscription Idea 49:44 - BF6 Isn't Unpopular Because Of Microtransactions 53:17 - The Alpha Isn't Until August 7th And That's Annoying 56:50 - Hell Let Loose: Vietnam Got Pushed Back 58:08 - Dak To Airfield First Impressions 1:00:08 - Napalm Strikes And Viet Cong Tunnels 1:01:57 - Why The First Beta Was A Disaster 1:04:39 - If Every Map Is Jungle, This Game Is DOA 1:08:02 - Free Crossplay Weekend, Ends Monday 1:09:40 - Will WARDOGS Have Controller Support? 1:11:55 - Battlefield 6 Season 4: Naval Warfare Finally 1:13:00 - Tsuru Reef Is Big, Bright, And Actually Fun 1:14:57 - The Bloom In This Game Is Infuriating 1:16:41 - Driving The New Warship 1:17:21 - Wake Island And The Top Gun Crossover 1:20:39 - RedSec Added Bots. Obviously. 1:21:02 - Aircraft Carrier Facts Nobody Asked For 1:23:13 - WARDOGS Is The Only Thing That Matters 1:25:11 - What We've Been Playing: Climbing Out Of Bronze 1:26:30 - Stellaris Nomads 1:27:52 - Do NOT Play Star Wars: The Old Republic 1:29:14 - Jake's Tarkov Problem 1:30:57 - Seasonal Character, Wipes, And Cheaters 1:33:39 - Why It's Been A Slow Few Months For Games 1:36:17 - If You Play Shooters, You Have To Play Tarkov 1:37:20 - Star Wars Zero Company (No Co-Op, Sadly) 1:38:25 - Outro + Patreon Hangouts 1:39:30 - Oh, And Black Ops 7 SBMM News _Note: timestamps may be slightly misaligned on podcast apps (but not on YouTube) due to dynamic ads._ The podcast is available wherever you listen to podcasts, and ad-free & early access versions - as well as bonus episodes - are available to all of our Patreon (https://www.patreon.com/thedropshot) supporters. We stream the podcast live on our YouTube (https://www.youtube.com/c/thedropshotpodcast) every Saturday morning at ~9 o'clock Pacific Time. We typically start the stream 30 minutes early to answer viewer questions, banter, and chat. Links for everything are below. Thanks for checking us out!
As someone who herself lost 100 lbs while in the ENM lifestyle, Brenna shares the ups and downs of a major weight loss. We discuss the "dad bod" phenomenon, the importance of self-confidence, and a few considerations people need to make BEFORE beginning a weight loss journey. For more information on our products and services, visit us today at https://betterbedroombody.com Get 15% off your first Shivers gummies order with code FPS at https://shivers.store Get 10% off your first Promescent order at https://promescent.com/fps Join us for bonus content and support the show at https://patreon.com/frontporchswingers Join us for our Vegas hotwife nights: https://members.frontporchswingers.com
Call of Duty | Wardogs | Modern Warfare 4 | Arena Breakout Infinite | Gray Zone Warfare More FPS News #podcast #gaming #fps Welcome to "The Scope," your ultimate FPS gaming podcast! Join us for the latest news, trends, and updates in the world of First Person Shooters. Whether you're a seasoned player or just starting out, our passionate hosts cover everything from new releases to gaming strategies. Dive into the action-packed universe of FPS games with us!Buffnerd GamingChannel: https://www.youtube.com/channel/UCUv67t-1w4i5NJhG3T1vtmgTwitter: https://twitter.com/BuffNerdGaming1BlueTheRobot: Channel: https://www.youtube.com/c/BlueTheRobotTwitter: https://twitter.com/bluetherobotCrash:Discord: https://discord.gg/4HZxRx3MkFTwitch: https://www.twitch.tv/crash8 Twitter: https://twitter.com/fps_crash
Check out Cam's latest novel / audio drama here! Doom: Revelations is so much more than an expansion. In this episode, we passionately argue that this is in fact the fifth modern Doom game in the last decade, an endlessly inventive, engaging, and replayable game in its own right that officially elevates the modern era of Doom into the same conversation as Halo during the Bungie years. Topics include: an ever-evolving endgame, signature weapons, the challenges of FPS boss fights, the secret to replayable campaigns, and how this game is a sequel to every single modern Doom game at the same time. LINKS: Patreon, YouTube, Spotify, Instagram Feedback & Theories: secondbreakfastpod@gmail.com 00:00 Thesis / Impressions 04:10 Late Stage God of War 07:13 Destiny 2 (Art Direction) 08:30 Dark Souls (Difficulty) 10:59 The Ethos of Modern Doom 14:02 Signature Weapons 17:03 Uniquely Good Boss Fights 18:51 The Secrets of Replayability 21:25 Endless Endgame 27:18 Sequel to Eternal / Ancient Gods 29:41 Sequel to The Dark Ages 33:47 The Elephant in the Room 37:09 The Successor to Halo 40:09 Episode 666 / Closing Thoughts
The Gamertag Radio crew reviews Halo Campaign Evolved! Danny, Parris, and Riana break down the Unreal Engine 5 visuals, modern mechanics like sprint, and the brand new bonus prequel missions featuring Sergeant Johnson. Plus, they share platform performance details and discuss what this means for the future of Halo.Episode Highlights Visuals & Engine: Built in Unreal Engine 5 (4K, 60+ FPS) featuring improved visuals, richer sound, and added environmental details like wildlife. Modern Mechanics: Adds sprint, clamber, ground pounds, vehicle hijacking, and improved enemy AI. Rebalanced Arsenal: Weapons feel refreshed; the Needler and Needle Rifle are far more effective, while the iconic pistol remains strong. Bonus Prequel Levels: Set one year before the main story, these new missions omit Cortana, feature Sergeant Johnson, and support chaotic four-player co-op. Performance: Plays great on PC and ROG Ally X, but performance is poor on the original white ROG Ally. No Microtransactions: All cosmetics, such as alternate Master Chief skins, are earned strictly through gameplay. Connect with us: Danny Peña Parris Lilly Pete Toledo Riana Manuel-Peña Send us questions - fanmail@gamertagradio.com | Speakpipe.com/gamertagradio or 786-273-7GTR. Join our Discord - https://discord.gg/gtr chat with other GTR community member.
//The Wire//2100Z July 22, 2026// //ROUTINE// //BLUF: IRANIAN TARGETING OF AMERICAN BASES REMAINS SIGNIFICANT. MISSILE THREAT ISSUED FOR LA GUARDIA LAST NIGHT, REPORT QUICKLY DEEMED NOT CREDIBLE.// -----BEGIN TEARLINE------International Events-Kuwait: Strikes on American positions continue to degrade American capabilities. The logistical hub at Camp Doha in Kuwait City was struck overnight, along with possible strikes at the remote HIMARS launch sites in near the Iraqi border. Iran also claimed to have struck the AN/FPS-117 Early Warning radar array at Ahmed al-Jaber Airbase.Analyst Comment: Unlike all of the other strikes, this site cannot be independently verified or geolocated using current satellite imagery, so it might be a newly constructed site or the Iranians got the names of bases mixed up.Saudi Arabia: Iranian missile strikes were reported throughout the morning, with unknown success. The oil terminals at Yanbu as well as American positions at Prince Sultan Airbase were targeted, with unknown effect. Jordan: American positions continue to be struck at Muwaffaq al Salti Airbase, in the vicinity of the strike that killed 3x service members last week. In central Jordan, King Faisal Airbase was targeted as well, with satellite imagery confirming that as of this morning, one of the structures targeted was still on fire. Down south, the American positions at King Hussein International Airport were targeted, as the Iranians attempted to strike the American refueling aircraft that have been parked on the apron for several days. Israeli citizens across the border observed smoke over the mountain ridge in the vicinity of this airport.Analyst Comment: Now that the Iranians know that their targeting is effective at Muwaffaq, they will continue targeting the same location over and over again. Same with Tower 22, the far-flung base in Jordan's northeastern quadrant, where extensive damage has been noted. Also, the Iranians claim to have unveiled a comparatively new missile system during this wave of attacks; Iran used many of their low-grade missiles during the early days of the war, and now they are occasionally pulling out the newer missiles which have more advanced maneuverability during the terminal phases of the warhead's flight. This might be a contributing factor for why missile interceptions are completely non-existent during some attack waves.-HomeFront-New York: Last night, a security incident was reported at La Guardia after a MANPAD alert was issued for the airport. Port Authority Police alerted the tower regarding a threat they had received regarding the potential use of a shoulder-fired Surface-to-Air missile launcher being used to target a civilian airliner. The report was deemed to be not credible almost immediately afterwards, but the alert was still sent out over the radio to all aircraft in the vicinity.-----END TEARLINE-----Analyst Comments: Sometimes, a few seconds of radio traffic is very illuminating. Listening to the very brief radio exchange between the tower and a Delta flight explaining the issue in New York, perhaps the most telling and concerning detail is that La Guardia possibly has a "MANPAD alert" procedure now. From a counterterrorism perspective this is a good thing, but it's also very disconcerting that this contingency is something that is not only actively being planned for...but already has a procedure drawn up for it. Granted, there's not a whole lot that a commercial airline pilot can do with that information while trying to land, and the rather nonchalant affirmation that no issues have been reported "yet" is only slightly reassuring. But this does go to show that threats within the homeland don't take a break just because eastern Europe and the Middle East are on fire, and the threats to commercial aviation are always on the menu even if most of the concerns are not credible. However, it only takes one credible threat for a very bad day to occur, and this target vector is the epitome of the old counterterrorism saying: We have to be successful in defending ourselves every single time, but the terrorist has to succeed only once.Analyst: S2A1 Research: https://publish.obsidian.md/s2underground NomadNet: 5fa68c88be727a0e1a250a75e5e79269 Disclaimer: No LLMs were used in the writing of this report. //END REPORT//
You and me and the deep blue sea!For this episode, we have randomly selected the 1-to-4 player cooperative roguelite FPS developed by DoubleMoose Games and published by The Arcade Crew. Based on their previous titles and their self-description of being "three-dimensional organic meat automatons" we can tell this Sweden-based developer has a sense of humor... but the tone of this shooter about descending the depths seems decidedly darker. Being both multiplayer and a roguelite should stack the odds against it harnessing the hearts our hosts, but they are still honor-bound to give it a fair chance and at least an hour of their time. When the time is up, will they be compelled to continue their quest or will they be relieved to let a stinker sink to oblivion? Dive in and discover their destiny!What do you think? Let us know!Check out all our links here:https://linktr.ee/tc1h1dThanks for taking this ride with us :-)
Halo: Campaign Evolved is a top-to-bottom remake of the iconic Halo: Combat Evolved. What happens when a 25-year-old FPS is modernized with quality-of-life improvements, a handful of new missions all built on the bones of an already good game? Does this warrant another playthrough, or is it purely a novelty to play on PlayStation 5?(00:00) Creature Cast Intro(2:34) Halo: Campaign Evolved review(50:19) OutroWebsite:https://www.consolecreatures.com/Like and follow us on Social Media:Bluesky: @consolecreatures.comYouTube: @ConsoleCreaturesTwitter: @ConsoleCreatureFacebook: @RealConsoleCreatureInstagram: @ConsoleCreaturesThreads: @Consolecreatures
Keywordsgaming, hardware, taxes, Warzone, AAA games, Battlefield, live service, gaming experience, player engagement, competitive play, AAA games, game development, economics, DLC, player engagement, map design, FPS games, content progression, battle pass, RedSecSummaryIn this episode, the hosts discuss various aspects of gaming, including the appreciation of gaming hardware, the financial implications of taxes on gamers, and the evolution of popular games like Warzone and Battlefield. They delve into the challenges faced by the AAA gaming industry, the impact of live service models, and the technical issues that affect the gaming experience. The conversation highlights the balance between competitive play and fun, as well as the importance of player engagement in the future of gaming. In this conversation, the hosts delve into the current state of AAA games, discussing the lingering issues in popular titles like Battlefield and Call of Duty. They explore the economics behind game development, the impact of DLC on player engagement, and the importance of map design in enhancing player experience. The discussion also covers the challenges of content progression in FPS games, the effectiveness of battle passes in retaining players, and the specific improvements needed in RedSec to enhance gameplay.TakeawaysThe value of gaming hardware can appreciate over time.Tax implications can significantly affect gamers' finances.Warzone's evolution has led to a debate on competitive balance.Fun and competitive play are often at odds in gaming.AAA gaming is facing significant challenges in delivering quality games.Battlefield's struggles highlight community expectations.Live service models can dilute game quality and player experience.Technical issues can frustrate gamers and impact enjoyment.Player engagement is crucial for the success of games.The gaming industry must balance profitability with player satisfaction. AAA games are struggling with lingering issues.Game development economics dictate profit margins.DLC can both engage and alienate players.Map design significantly affects player experience.Call of Duty excels in content and progression.Battle Pass systems need to be more player-friendly.RedSec requires improvements in squad sizes and vehicle balance.Player engagement metrics can be misleading.Content updates should be frequent and meaningful.The gaming community desires more transparency from developers.TitlesThe Future of Gaming: Balancing Fun and CompetitionWarzone's Downfall: A Case Study in Game Designsound bites"RIP wars on mobile officially.""They've made profit.""I just want him to do something."Chapters00:00 Introduction and Weekend Recap02:20 Gaming Hardware and Market Trends05:52 Taxation and Financial Implications in Gaming08:21 Warzone's Evolution and Competitive Balance17:14 AAA Gaming Industry Challenges26:06 Future of Gaming and Market Dynamics27:48 The Downfall of AAA Games29:39 The Battlefield Dilemma31:44 Metrics of Success in Gaming36:31 The Impact of Live Service Models42:40 The Economics of Game Development47:54 DLCs and Player Engagement56:19 Content Diversity in Gaming57:18 Battlefield's Progression Issues01:00:15 Vehicle Handling and Consistency01:02:12 Battle Pass Concerns01:04:17 Gaming Experience Enhancements01:07:06 Future Gaming Plans01:09:02 Community Feedback and Game Changes01:11:01 RedSec Gameplay Dynamics01:13:53 Map Design and Player Experience
Get 20% off DeleteMe by going to https://joindeleteme.com/friends and use code FRIENDS to protect your privacy! -- Try Factor at https://www.factormeals.com/fps50off with code fps50off to get 50% off and free daily greens per box, with new subscription only, while supplies last until 09/27/2026. Thanks to Factor for sponsoring FPS! -- Head to https://buyraycon.com/friendsOPEN to get up to 20% off Raycon audio products. Thanks to Raycon for sponsoring FPS! -- Timestamps: 00:00 Intro ft. Guest Host Chris Grant 18:08 Xbox Discussion 32:50 DeleteMe (ad) 35:46 Factor (ad) 38:11 State of the Industry Discussion 01:06:22 Raycon (ad) 01:09:10 User Question 01:18:11 Ralph and Lucy have been playing The Blood of Dawnwalker 01:28:27 Jake's been playing AC Black Flag Resynced 01:37:58 Chris's been playing Doom The Dark Ages Revelations and Deltarune 01:49:33 Lucy's been playing Denshattack! 01:55:28 Steam Machine Discussion 02:12:15 Show and Tell Learn more about your ad choices. Visit megaphone.fm/adchoices
Call of Duty | Wardogs | Modern Warfare 4 | Arena Breakout Infinite | Gray Zone Warfare More FPS News #podcast #gaming #fps Welcome to "The Scope," your ultimate FPS gaming podcast! Join us for the latest news, trends, and updates in the world of First Person Shooters. Whether you're a seasoned player or just starting out, our passionate hosts cover everything from new releases to gaming strategies. Dive into the action-packed universe of FPS games with us!Buffnerd GamingChannel: https://www.youtube.com/channel/UCUv67t-1w4i5NJhG3T1vtmgTwitter: https://twitter.com/BuffNerdGaming1BlueTheRobot: Channel: https://www.youtube.com/c/BlueTheRobotTwitter: https://twitter.com/bluetherobotCrash:Discord: https://discord.gg/4HZxRx3MkFTwitch: https://www.twitch.tv/crash8 Twitter: https://twitter.com/fps_crash
Much to Zach's chagrin but Jack's delightful, this installment of Hardly Focused stinks! Literally. People are panicking over the "diarrhea parasite," and it seems that Taco Bell's lettuce is to blame. Take Dr. Drew's advice, and don't worry about it! Also discussed in this episode: Canada is on fire, and it's turning Boston's skies yellow. Jack prefers gaming in 4K quality mode over 60 FPS performance mode. The "Capitol Hill gas leak," courtesy of Senate minority leader Chuck Schumer's ass. FOLLOW and SUBSCRIBE! https://hardlyfocused.com/subscribe Learn more about your ad choices. Visit megaphone.fm/adchoices
We have learned several times recently that there is a HUGE difference between having specific expectations at a lifestyle event versus setting intentions before going. What are the differences, and where can each get you in trouble in your lifestyle journey? Set up your FREE consultation call with us to start taking your health and wellness seriously today! https://revitaglowmeds.com Get 10% off your first Promescent order when you visit https://promescent.com/fps Get 15% off your first order of Shivers gummies with code FPS at https://shivers.store Try Kasidie FREE for a month! Click on the Kasidie banner at https://frontporchswingers.com
When Doom released to the masses in the early 90s, the newly minted "first person shooter" (FPS) genre became insanely popular, with countless developers attempting to capture the lighting in a bottle that Id Software became famous for. While Doom would feature hordes of demons just waiting to be mowed down, other games took different approaches to bringing their own first person shooter experiences to the public, with one of the most surprising entrants into the FPS market being LucasArts. The adventure game behemoth decided to use its own Star Wars intellectual property to create the kind of first person shooter that simply hadn't existed up to this point in history, which is what would result in the creation of the DOS FPS classic, Dark Forces. Learn how the game was made, discover why LucasArts decided to make a first person shooter set in the Star Wars universe in the first place, and listen in as we determine whether it's still worth your time to take the fight to the Empire, FPS style, even today. Join the discussion on Discord! Want more Classic Gaming Today? Sign up as a patron at Patreon.com/ClassicGamingToday!
The bottom continues to fall out from the games industry. Approximately five impending studio closures was simply not enough. Here comes PlayStation who is shutting down their entire physical game operation. It's never been more clear than right now that many of us who have been gaming since the 90s are now truly considered "the old guard" as we continue to face off against staggering changes at a breakneck speed. Timestamps: Please keep in mind that our timestamps are approximate, and will often be slightly off due to dynamic ad placement. 0:00 - Intro3:31 - Checking in with the Dukes22:50 - Xbox announces more console price increases48:38 - Is Xbox pulling third party deals from Game Pass?55:26 - PlayStation goes all digital1:18:37 - The Bungie cuts are official1:34:27 - Will GTA 6 be 60 FPS?1:38:57 - Oblivion Remastered gets Switch 2 port details1:41:07 - What We're Playing2:06:09 - Xbox is eyeing Undead Labs for closure2:29:24 - Xbox debates canceling Blade and shutting down Arkane2:58:52 - Xbox pulls funding from Project Fantasy3:12:12 - Wrap up Learn more about your ad choices. Visit podcastchoices.com/adchoices
Head to https://buyraycon.com/friendsOPEN to get up to 20% off Raycon audio products. Thanks to Raycon for sponsoring FPS! -- A huge thanks to Kyle Bosman for joining us. Pleas do check out his stuff over at https://www.youtube.com/@UCSJL4mrS3z_nnvsJ-gkOQ5A -- If you wanna check out our newsletter, you can do so here: https://friendspersecond.substack.com/ Listen to the Friends Per Second Podcast on your favourite podcast platform: https://linktr.ee/friendspersecond Follow on Instagram: https://www.instagram.com/friendspersecond -- Let's meet our hosts! - Jake Baldino (aka the Before You Buy Guy) is pretty much the most watched reviewer on YouTube across both Gameranx and his personal channel (https://www.youtube.com/c/JakeBaldino). If you're obsessed with Delorians, The Mummy and Pizza you can discuss that stuff with him directly over on Twitter: @JakeBaldino - Lucy James was a Senior Producer at Gamespot for more than a decade before striking out on her own, founding https://lookingfor.game/, a newsletter and games discovery platform that serves up cool suggestions (and demo codes) directly to your inbox. She also co-hosts the annual Summer Games Showcase alongside Geoff Keighly. - Skill Up used to work at McDonalds but he got fired for skimming too many chicken nuggets. He says he regrets it since he hasn't had a better job since. Learn more about your ad choices. Visit megaphone.fm/adchoices
Call of Duty | Wardogs | Modern Warfare 4 | Arena Breakout Infinite | Gray Zone Warfare More FPS News #podcast #gaming #fps Welcome to "The Scope," your ultimate FPS gaming podcast! Join us for the latest news, trends, and updates in the world of First Person Shooters. Whether you're a seasoned player or just starting out, our passionate hosts cover everything from new releases to gaming strategies. Dive into the action-packed universe of FPS games with us!Buffnerd GamingChannel: https://www.youtube.com/channel/UCUv67t-1w4i5NJhG3T1vtmgTwitter: https://twitter.com/BuffNerdGaming1BlueTheRobot: Channel: https://www.youtube.com/c/BlueTheRobotTwitter: https://twitter.com/bluetherobotCrash:Discord: https://discord.gg/4HZxRx3MkFTwitch: https://www.twitch.tv/crash8 Twitter: https://twitter.com/fps_crash
Kermit and Listra return! Listra's FPS trauma and drama plus Kermit's Steam summer sale wishlist.A podcast by gamers for the gaming community. Join us each week as we talk new games, old games, AAA and Indie. No drama. No guilt. Games, a little real life and community. Gaming is an all inclusive world.Find us onhttps://twitter.com/3rdfactionshowhttps://twitter.com/MsListra https://bsky.app/profile/mslistra.bsky.social and Twitch.tv/Mslistrahttps://twitter.com/RPGamer4life and Twitch.tv/RPGovanTwitch.tv/organizedchaosgamesDiscord Serverhttps://discord.gg/jNYr9mVNN7You can email the show onthethirdfactionshow@gmail.comPatreonhttps://www.patreon.com/cw/thethirdfactionshow
Press XJoin the Press X Discord: https://discord.gg/MAXtvmv2rwTopics:Is Switch 2 the last console to have physical games? Physical disc production ending in January 2028 for new games releasing on PlayStation consoles https://blog.playstation.com/2026/07/01/physical-disc-production-ending-in-january-2028-for-new-games-releasing-on-playstation-consoles/PS3 and Vita digital stores are shutting down for real this time https://blog.playstation.com/2026/07/01/an-update-on-playstation-store-for-ps3-and-ps-vita/Xbox is testing a disc-to-digital feature that digitizes a physical game collection https://x.com/Wario64/status/2072376537664413900Switch 2 seems to be getting a revised LCD panel from Sharp https://bsky.app/profile/ninpatentswatch.bsky.social/post/3mpgynym2ok2kMario Kart World ver. 1.7.0 update adds 2 new routes to Knockout Tour and stickers in Photo Mode https://www.nintendo.com/us/whatsnew/mario-kart-world-update-adds-two-new-routes-to-knockout-tour-and-more/Oblivion Remastered launches August 11 on Switch 2 as a real physical game, runs at 900p 30 FPS handheld and 1080p 30 FPS docked with DLSS https://elderscrolls.bethesda.net/en-US/news/2AQF1Pmy3m8GGT8s0RTkJT/the-elder-scrolls-iv-oblivion-remastered-nintendo-switch-2-launch-dateLove and Deepspace canceled the release of a new character because some fans did not like him. The fan base is imploding. Reviews on all app stores are abysmally low. Law suits are in the works. I have not seen drama of this level in a very long time. https://www.polygon.com/love-and-deepspace-bring-back-valko/ Y'all don't actually have to talk about this lol Questions from Discord: Bailey: I have never played GTA 5, but all the hype around GTA 6 has me wanting to play it day one. Do you think it's necessary to play 5 first? Will I appreciate or enjoy 6 more if I've already experienced 5, or would it be fine to skip 5 and go straight to 6? I definitely want to have the best experience possible but there isn't a ton of time between now and the new one coming out and I have a lot of games on my list to play.
This time on Retro Fandango, we're diving into Bloodshed, the indie FPS from com8com1 Software that blends classic '90s boomer shooter action with modern roguelite progression and Survivors-style gameplay. We break down how this retro-inspired shooter combines relentless hordes, satisfying gunplay, and permanent upgrades into a surprisingly addictive experience. Is Bloodshed a worthy evolution of old-school FPS design, or does it get lost in the crowd? Join us as we take a closer look at one of indie gaming's bloodiest hidden gems.#RetroFandango #Bloodshed #BoomerShooter #RetroGaming #IndieGames #FPS #FirstPersonShooter #Roguelite #SurvivorsLike #PCGaming #SteamGames #GamingPodcast #VideoGames #ClassicFPS #IndieFPS
An INSANE forum conversation has us once again questioning the rise of raw play... We share recent stats on STI rates with and without condoms, and our theories on why so many in the lifestyle seem to want to play with fire. Get 15% off your first order of Shivers Gummies with code FPS at https://shivers.store Try Kasidie FREE for a month! Click on the Kasidie banner at https://frontporchswingers.com Join us for our monthly Real Hotwives of Las Vegas event! https://members.frontporchswingers.com Let's get you started on a new health and wellness journey! https://revitaglowmeds.com
Call of Duty | Wardogs | Modern Warfare 4 | Summer Game Fest More FPS News #podcast #gaming #fps Welcome to "The Scope," your ultimate FPS gaming podcast! Join us for the latest news, trends, and updates in the world of First Person Shooters. Whether you're a seasoned player or just starting out, our passionate hosts cover everything from new releases to gaming strategies. Dive into the action-packed universe of FPS games with us!Buffnerd GamingChannel: https://www.youtube.com/channel/UCUv67t-1w4i5NJhG3T1vtmgTwitter: https://twitter.com/BuffNerdGaming1BlueTheRobot: Channel: https://www.youtube.com/c/BlueTheRobotTwitter: https://twitter.com/bluetherobotCrash:Discord: https://discord.gg/4HZxRx3MkFTwitch: https://www.twitch.tv/crash8 Twitter: https://twitter.com/fps_crash
We went to this year's Hotwife Palooza in Tucson! And the event itself was amazing! Great staff, friendly attendees, well organized. Yet we found ourselves questioning, from a purely personal perspective, if we belonged in that environment. Listen to find out what we mean! Get bonus content and support the show at https://patreon.com/frontporchswingers Start your peptide journey with your free consultation call at https://revitaglowmeds.com Try Shivers gummies and get 15% off! Use code FPS at https://shivers.store Try Kasidie FREE for a month! Click on the Kasidie banner at https://frontporchswingers.com
Head to https://buyraycon.com/friendsOPEN to get up to 15% off Raycon audio products this holiday season. Thanks to Raycon for sponsoring FPS! -- Timestamps: 00:00 Intro / Housekeeping 07:50 Sony State of Play 34:14 Raycon (ad) 35:50 Interview with God of War: Laufey Director Ariel Lawrence 1:04:41 Summer Game Fest Showcase 1:37:17 Interview with That's No Moon, CCO Taylor Kurosaki and Game Director Jacob Minkoff 2:13:04 Xbox Showcase 2:29:05 Wrap Up -- If you wanna check out our newsletter, you can do so here: https://friendspersecond.substack.com/ Listen to the Friends Per Second Podcast on your favourite podcast platform: https://linktr.ee/friendspersecond Follow on Instagram: https://www.instagram.com/friendspersecond -- Let's meet our hosts! - Jake Baldino (aka the Before You Buy Guy) is pretty much the most watched reviewer on YouTube across both Gameranx and his personal channel (https://www.youtube.com/c/JakeBaldino). If you're obsessed with Delorians, The Mummy and Pizza you can discuss that stuff with him directly over on Twitter: @JakeBaldino - Lucy James is a Senior Producer at Gamespot. She's actually, like, experienced and credentialed and has real life skills and stuff, while the rest of the gang would be funemployed if the YT algorithm didn't kiss them for random, inexplicable reasons. - Skill Up used to work at McDonalds but he got fired for skimming too many chicken nuggets. He says he regrets it since he hasn't had a better job since. Learn more about your ad choices. Visit megaphone.fm/adchoices
Is getting into the lifestyler a motivator for people to work on their health and fitness? We share our personal and anecdotal experiences. Plus, why is nobody talking about the fact that visceral fat increases men's risk of ED related issues by four times?? Get 15% off the amazing pleasure enhancing gummies you're hearing all about! Use code FPS at https://shivers.store Schedule your free consultation call with us today to discuss peptides, weight loss meds and more! https://revitaglowmeds.com Join us for an upcoming event: https://members.frontporchswingers.com Try Kasidie FREE for a whole month! Click on the Kasidie banner at https://frontporchswingers.com Join us for bonus episodes of FPS right from our living room! https://patreon.com/frontporchswingers
If we've heard it once, we've heard it a million times: "Cliques in the lifestyle are a problem!" Is that the case? As two self-proclaimed non-joiners, we dissect the world of cliques in the lifestyle, when they can be of value, and when they become downright toxic. Plus, what's the most common form of gatekeeping in the hotwife lifestyle? Try Shivers gummies and get 15% off today with code FPS at https://shivers.store Get all access to Kasidie FREE for a month! Click on the Kasidie banner at http://frontporchswingers.com Join us for an upcoming event: https://members.frontporchswingers.com Schedule your FREE consultation call with us today at https://revitaglowmeds.com
FPS Through the Ages draws to an explosive close! Join the HG101 gang as they discuss and rank a 2020s first-person shooter that blends classic and modern FPS elements into a gory cybernetic thrill ride. Then stick around as returning special guest Sean Seanson joins for Magical Date: Doki Doki Kokuhaku Daisakusen, an arcade game about wooing girls based on your ability to do basic math! This weekend's Patreon Bonus Get episode will be RIPPLE DOT ZERO — a Flash-based penguin platformer, inspired by the 16-bit era! Donate at Patreon to get this bonus content and much, much more! Follow the show on Bluesky to get the latest and straightest dope. Check out what games we've already ranked on the Big Damn List, then nominate a game of your own via five-star review on Apple Podcasts! Take a screenshot and show it to us on our Discord server! Intro music by NORM. 2026 © Hardcore Gaming 101, all rights reserved. No portion of this or any other Hardcore Gaming 101 ("HG101") content/data shall be included, referenced, or otherwise used in any model, resource, or collection of data.
What's this?! The esteemed 'Belgium Beauty' as part of the Dukes in 2026?! Indeed, it's Lock and Matty here to sprinkle some extra fun in the world of Xbox where the news never stops moving! This week, we have two types of leaks: A fun release window... and an actual game ahead of its launch. Starting with the fun stuff, Gears Of War: E-Day is looking to take a slot in what will, no doubt, be an extremely busy September. How it leaked is the fun part! In the not so fun stuff, Xbox themselves leaked Forza Horizon 6 via Steam. Fortunately, it is very close to the game's launch, but there is more to this story. How did this go down and should there be any panic? Let's dive in!Please keep in mind that our timestamps are approximate, and will often be slightly off due to dynamic ad placement.0:00:00 - Intro0:03:42 - Health Is Wealth0:08:37 - The Xbox-Discord partnership is official0:21:22 - Forza Horizon 6 leaks online ahead of launch0:28:22 - Ken Levine on why Judas took a decade to make0:47:07 - eBay rejects Gamestop's offer0:55:40 - Lies Of P sequel in full production1:10:19 - PlayStation first party is diving into the world of AI tools1:31:10 - SEGA cancels Super Game1:38:02 - The Bungie acquisition gets uglier1:48:57 - 007 First Light claps back at FPS jokes2:00:38 - Switch 2 price hike is here2:06:12 - Game sales update2:09:47 - What We're Playing2:57:03 - Gears Of War E-Day appears set for September3:06:45 - Wrap up Learn more about your ad choices. Visit podcastchoices.com/adchoices
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