Podcasts about DSV

Joshua Neuloh ist zu Gast und hilft dabei, einen Blick zurück auf die EM-Woche in Paris zu werfen. Gespickt mit Behind-the-Scenes Infos vom Pool-Deck thematisieren wir natürlich den Weltrekord von Johannes Liebmann, ziehen ein umfangreiches Fazit der Wettbewerbe und versuchen das an der Medaillenzahl und Final-Teilnehmer-Zahl gemessene sehr gute Ergebnis des DSV realistisch einzuordnen! Für Fragen, Kommentare, Anmerkungen nutzt Social Media oder die gute alte E-Mail: andre(at)swimcast.de ----- Episodenbild: Tino Henschel Photo Musik: www.zapsplat.com

#365: Endlich wieder Biathlon!

Aktiekursen dykker hos den danske logistikgigant DSV. Analytikerne betragter kursfaldet som en overreaktion, mens investorerne ser stik modsat på udviklingen. Det store spørgsmål er, om tilliden til topchef Jens Lund ved at smuldre i en tid hvor virksomheden står overfor massive udfordringer. Gæst: Søren Linding, debatredaktør og kommentator på Finans Vært: Kasper Søegaard, podcastredaktørSee omnystudio.com/listener for privacy information.

"Das wird toll" sind die Worte mit denen Merle die Europameisterschaften in prägnanter Kürze ankündigt. 36 Aktive mit 81 Einzel- und 9 Staffelstarts werden uns ab Montag im Pariser Centre Aquatic Olympique vorzüglich unterhalten. Wir blicken auf die spannendsten deutschen Rennen, wagen Medaillenvorhersagen, sprechen über die Athlet*innen abseits des großen Rampenlichts und stimmen euch ein auf sieben unterhaltsam-spannende Wettkampftage! Für Fragen, Kommentare, Anmerkungen nutzt Social Media oder die gute alte E-Mail: andre(at)swimcast.de ----- Episodenbild: Tino Henschel Musik: www.zapsplat.com

Sommeridyllen er for alvor forbi for Novo Nordisk og topchef Mike Doustdar. Den danske medicinalgigant offentliggør i denne uge sit halvårsregnskab, og dermed får aktionærerne et unikt indblik i Novos maskinrum. Jungletrommerne lyder, at en opjustering er på vej. Samtidig er det kun en uge siden, at Novo Nordisk fik et gigantisk kurshug efter dårligt nyt om selskabets hjertemedicin. Det rejser spørgsmålet: Hvor står Novo Nordisk egentlig? Det diskuterer panelet i denne uges udgave af Bundlinjen, hvor DSV's voldsomme kursnedtur også bliver vendt i studiet. Bemærk at Bundlinjen fremover udkommer hver tirsdag morgen. Ugens panel: Søren Linding, debatredaktør og erhvervskommentator på Finans Simon Bendtsen, chefredaktør på Finans Niels Lunde, erhvervskommentator på Finans Vært: Rasmus Bendtsen, redaktør på Finans Podcastredaktør: Kasper SøegaardSee omnystudio.com/listener for privacy information.

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

Et brag af en handelsdag venter onsdag, hvor den danske transportkæmpe DSV blandt andet har leveret friske tal. Senere på dagen venter regnskaber fra blandt andet Alphabet, Tesla og IBM, der kan definere om tech-aktierne skal op eller ned. Millionærklubben diskuterer og dissekerer derfor tallene, og vender hvad det kan betyde for investorerne. Vi vender også nye kinesiske AI-modeller, der har rystet markedet, og undersøger om der er grundlag for at rykke rundt på porteføljen. Med i studiet er investeringsstrateg hos Saxo Bank Oskar Barner Bernhardtsen og investeringschef i teknologifonden Techwave, Benjamin Bjerre. Vært: Adam Geil See omnystudio.com/listener for privacy information.

The ASX 200 rose 44 points to 8,806, up 0.5%, after a slow start, with the banks finding their feet. For the week, the ASX 200 fell 38 pts. CBA rose 0.5% and ANZ gained 0.8%. The Big Bank Basket rose to $281.06 (0.6%). Other financials also firmed, with MQG up 0.9% and NWL rising 2.2%. ZIP rallied 2.0%. REITs were better, with GMG up 0.2% and CHC rising 2.2%. In the industrials, we saw some profit-taking, with TLS under pressure now that Vicki Brady is back on board from her holiday. WOW and COL were also in profit-taking mode after defensive buying earlier this week. Elsewhere, the healthcare sector pulled back after a strong week, with CSL down 2.1% and RMD falling 0.9%. Technology stocks were under pressure, unable to capitalise on the improved outlook for US software companies, with XRO down 1.3% and WTC falling another 1.8%. The All-Tech Index fell 0.6%.Resource stocks were the heroes today, as the iron ore miners pushed higher. BHP bounced 2.5%, RIO also recovered after yesterday's heavy falls, rising 3.8%, while FMG edged up 2.0%. Gold miners were back in the green after this week's weakness, with EVN up 3.4% and NST gaining 1.7%. Lithium stocks remained under a little pressure, with PLS up 0.4%. Oil and gas stocks eased, with WDS and STO both lower. Uranium stocks were stronger, with PDN up 4.1%, while SLX was one of the standout performers, soaring 9.4%.In corporate news, INA responded to media reports of an approach from PPC. BVS jumped after it upgraded its FY26 earnings. WTC fell despite reassuring the market on DSV relations. SFR up strongly on an increase in its Black Butte mine life. There was nothing of note on the local economic front.Asian markets were mixed with the Nikkei 225 up 1.7%, the Hang Seng up 1.3%, and the CSI index down 1.0%. Kopsi up 4.2% ahead of SK Hynix debut. European futures were narrowly mixed.US futures were weaker with the Dow up 12 and the Nasdaq down 153. Marcus Today – Daily Market InsightsMarcus Today provides clear, practical commentary for self-directed investors – covering markets, portfolios, education, and decision-making without the noise.If you'd like to go further:Start a free 14-day trial of Marcus Today http://bit.ly/mt-trial-podcastJoin Marcus Today Use code MTPODCAST for 10% off http://bit.ly/mt-join-podcast-offerMT20 – Managed ETF Portfolio A professionally managed portfolio run by Marcus Padley and the team, using ASX-listed ETFs with active market timing. http://bit.ly/mt20-podcastPrinciples – How We Think About Investing A short video series on timing, behaviour, and decision-making. No stock tips. http://bit.ly/mt-principles-podcast—Disclaimer This podcast is general information only and does not consider your personal circumstances. It is not personal financial advice.

DSV har spist og spist nogle af sine største konkurrenter - og vokseværket har givet pote. Selskabet er blevet verdens største logistik-operatør, men sulten er langt fra stillet, og særligt et kontinent fylder alt for lidt i DSV-forretningen. Afrika. Og det skal der laves om på nu. Hør i denne Lyn-analyse, hvor stort potentialet er i Afrika for DSV, og hvilke store udfordringer, der ligger på verdens næststørste kontinent. Gæst: Kresten Andersen, journalist, Finans. Vært: Mads Ring. Foto: DSV. See omnystudio.com/listener for privacy information.

(01:00): Kan det have konsekvenser, hvis Danmark nedprioriterer den militære oprustning? Medvirkende: Tim Whyte, generalsekretær for Mellemfolkeligt Samvirke. (30:00): Udfører socialrådgivere deres arbejde forskelligt alt efter, hvorvidt de bliver optaget eller ej? Medvirkende: Trine Quist, formand for Dansk Socialrådgiverforening i Region Nord. (44:00): Hvad er problemet ved at staten holder hånden under konkursboerne? Medvirkende: Leif Tullberg, tidligere administrerende direktør i DSV. Værter: Laura Lin og Anne PhillipsenSee omnystudio.com/listener for privacy information.

Nick Lojevec je diplomirani inženir strojništva in mednarodni podjetnik, specializiran za logistične in industrijske rešitve na Bližnjem vzhodu. Svojo kariero je začel na Fakulteti za strojništvo v laboratoriju LFDT (Laboratorij za fluidno dinamiko in termodinamiko), kjer se je ukvarjal s področjem termodinamike in dinamike fluidov. Že več kot 12 let živi in dela v Združenih arabskih emiratih, kjer kot poslovni direktor za Bližnji vzhod pri irskem proizvajalcu industrijske opreme Combilift razvija logistične in materialno-transportne rešitve za številne industrije, med drugim prehransko industrijo, oil & gas sektor, logistiko in skladiščenje, železarne ter pristanišča. V tem času je sodeloval pri številnih pomembnih industrijskih projektih v regiji. Med drugim pri optimizaciji skladišč za prehransko industrijo, kjer lahko z naprednimi logističnimi rešitvami povečajo skladiščno kapaciteto tudi do 50 %, pri polavtomatizaciji nalaganja kontejnerjev v pristanišču Jebel Ali v Dubaju, ter pri razvoju popolnoma avtomatiziranega sistema za buffer in skladiščenje proizvedenega sladkorja, kjer se za transport uporabljajo 4D taxi sistemi. Ta projekt, izveden leta 2018 v ZAE, je bil eden prvih tovrstnih avtomatiziranih logističnih sistemov v regiji. Pri svojem delu sodeluje z nekaterimi največjimi podjetji na svetu, kot so Saudi Aramco, ADNOC, DP World, DSV, ter z vodilnimi oil & gas podjetji, ki črpajo nafto v regiji, in podjetji, ki zagotavljajo logistično in tehnično podporo tem operacijam. V Savdski Arabiji je sodeloval tudi pri velikih razvojnih projektih, kot so NEOM – The Line, Red Sea Project in Trojena. Njegovo delo pokriva številne trge v regiji, med drugim Združene arabske emirate, Savdsko Arabijo, Katar, Kuvajt, Oman, Bahrajn, Jordanijo, Egipt, Libanon, Irak in Šrilanko, kjer pomaga podjetjem izboljšati logistične procese, optimizirati skladišča ter uvajati napredne industrijske tehnologije. Svoje znanje, izkušnje in poslovne povezave, ki jih je skozi leta zgradil na Bližnjem vzhodu, deli tudi s podjetji, ki želijo uspešno vstopiti na trge Bližnjega vzhoda, ter jim pomaga razumeti poslovno okolje in priložnosti v tej regiji. Nick je tudi ustanovni član Društva Slovencev v ZAE, ki je bilo ustanovljeno leta 2023 v Dubaju z namenom povezovanja slovenske skupnosti v Združenih arabskih emiratih. Danes opravlja funkcijo predsednika društva.  Kot velik domoljub je skupaj s partnerjem v Sloveniji ustanovil podjetje Top Mop, sodoben čistilni servis, katerega cilj je prenesti visoko raven storitve in profesionalnosti, ki jo poznamo v Dubaju, na slovenski trg. Podjetje združuje tradicionalno industrijo čiščenja z modernim pristopom, visoko kakovostjo storitve in poudarkom na uporabniški izkušnji. V prostem času je strasten cestni kolesar, največ časa pa posveča tudi svoji družini, ženi Urši in 9-letni hčerki Hayi. Najljubši citati: HH Sheikh Mohammed bin Rashid Al Maktoum"Every morning in Africa, a gazelle wakes up, it knows it must outrun the fastest lion or it will be killed." "Every morning in Africa, a lion wakes up. It knows it must run faster than the slowest gazelle, or it will starve." "It doesn't matter whether you're the lion or a gazelle – when the sun comes up, you'd better be running."Najljubša knjiga: My Vision from HH Shaikh Mohammed bin Rashid Al Maktoum Najljubša serija: Suits Hobiji: Cestno kolesarstvo Najljubša hrana: Tortilije ki jih naredi zena Najljubši podjetnik: HH Shaikh Mohammed bin Rashid Al Maktoum Najljubša aplikacija: Chat GPT Zaključni nauki:·      Sam menim, da je izobrazba vse kar delaš nekje do 25 leta, šola, šport, hobiji, ·      Ne delaj vse za denar·      Mreženje

In dieser Folge sprechen wir über die schmale Grenze zwischen Erfolg und Misserfolg – besonders im Leistungssport. Unser Gast Korbinian Resch betreut seit fast zehn Jahren die Sparte Skilanglauf des DSV und gibt spannende Einblicke in seine Erfahrungen rund um die Olympiade. Mit etwas Abstand blicken wir auf Höhen und Tiefen dieser intensiven Zeit zurück. Dabei wird deutlich, wie nah Erfolg und Misserfolg oft beieinanderliegen – und welche entscheidende Rolle Osteopathie und therapeutische Begleitung dabei spielen können.

#354:

Verdens største logistikfirma, danske DSV, præsenterede onsdag planen for fremtiden. Investorerne har været nervøse. For om logistikkæmpen kunne tjene penge igen som tidligere, efter at man lavede kæmpeopkøbet af tyske DB Schenker sidste år. Og for om DSV er bliver overhalet indenom af Amazon eller andre tech-selskaber, der tager kvanteskridt i udviklingen af kunstig intelligens. Men DSV-topchef Jens Lund er knusende rolig. Hør i Lunde & Linding, om de er enige i, at DSV-bossen kan være rolig. Om han er manden, der kan føre DSV ind i fremtiden. Og om det er ok ikke at nævne klima og diversitet med stort set et eneste ord i sine målsætninger. Medvirkende: Niels Lunde, erhvervskommentator, Finans. Søren Linding, erhvervskommentator, Finans Vært: Mads Ring. Foto/Grafik: Thomas Lekfeldt/Anders Thykier.See omnystudio.com/listener for privacy information.

The ASX 200 fell another 40 points to 8630 (0.5%) as the banks came under intense pressure following the budget last night. Adding to the bank woes was the update from CBA, which shocked the market as bad debts rose and growth was sadly lacking. CBA fell an astonishing 10.4%, with the other three banks also falling hard on changes to housing policy in the budget. The CBA fall accounted for around 85 index points. The Big Bank Basket fell hard to $260.67 (-7.1%) as the Big Resource Basket soared 2.2%, overtaking the banks. BHP led resource stocks higher, hitting another record high, up 2.9%, and claiming the mantle back from CBA as the 'big Australian'. RIO also had a good day on the back of near-record highs in copper, with FMG also putting on the ritz. Given the fall in CBA, the index elsewhere had a good day.The gold miners were in demand, although bullion was relatively stable. EVN up 0.6%, and NST up 1.0%. Lithium stocks had a small break today, with PLS easing back 0.9%, as did LTR, but rare earth stocks were back in demand, with LYC up a further 2.0%. Energy stocks were mixed, with WDS up 0.4% and STO having a good day, up 1.6%, but uranium stocks eased back, with PDN falling hard on results. Coal stocks firmed.Industrials generally rose post-budget, with the REITs doing well. GMG up 1.4%, WES finding a base, up 0.4%, and even retail stocks looking a little firmer, JBH up 2.0%. One of the big winners was in the gaming space, with ALL updating the market with some latest numbers and rallying strongly, up 13.3%. Technology stocks were also in demand today. XRO rose after announcing some AI integration progress, although WTC was still on the nose. Healthcare stocks were also slightly better today, with CSL up 0.2% and RMD having a good day for a change, up 2.0%. Financials ex the banking sector were also firm, with AMP up 1.7% and GQG having a very good day, up 4.8%.In corporate news, CSL signed a flu vaccine deal in South America, PRN also doing well up 8.4%, after being awarded a mining contract, and TPW fell again after guidance was cut. In other news, WTC fell slightly after DSV confirmed it will transition away from cargo-wise and on to an in-house solution. ALL was a huge winner on a first half beat.Asian markets better with Japan up 1.2%, China up 0.5% and HK down 0.2%; The Kospi back up 2.4%US futures modestly higher, Dow futures down 2, Nasdaq up 149. European futures opening slightly higher. US PPI tonight.—Marcus Today – Daily Market InsightsMarcus Today provides clear, practical commentary for self-directed investors – covering markets, portfolios, education, and decision-making without the noise.If you'd like to go further:Start a free 14-day trial of Marcus Todayhttp://bit.ly/mt-trial-podcastJoin Marcus TodayUse code MTPODCAST for 10% offhttp://bit.ly/mt-join-podcast-offerMT20 – Managed ETF PortfolioA professionally managed portfolio run by Marcus Padley and the team, using ASX-listed ETFs with active market timing.http://bit.ly/mt20-podcastPrinciples – How We Think About InvestingA short video series on timing, behaviour, and decision-making. No stock tips.http://bit.ly/mt-principles-podcast—DisclaimerThis podcast is general information only and does not consider your personal circumstances. It is not personal financial advice.

Det kører for logistikgiganten DSV – bogstavelig talt. Eller det kommer det i hvert fald til. For tirsdag kunne selskabet med Jens Lund i spidsen berolige investorerne med, at den gamle DSV-maskine med stift blik på indtjeningen igen kommer op og køre for fuld fart efter den store øvelse med at integrere kæmpeopkøbet af tyske DB Schenker. Samtidig er selskabet dybt fokuseret på at udnytte kunstig intelligens og derved opnå store besparelser på effektivitet. Hør hvordan i denne Lyn-analyse fra Finans. Gæst: Søren Linding, erhvervskommentator, Finans. Vært: Mads Ring. Foto: presse/DSV.See omnystudio.com/listener for privacy information.

Pamela Anderson, I kender hende fra Baywatch, er landet i København som ambassadør for Pandora. Vi fanger hende... i sidste sekund. Men der er også en større anledning: Den danske smykkegigant har netop fremlagt regnskab for årets første kvartal - og det dykker vi ned i. DSV er samtidig under pres, efter at Amazon har lanceret Amazon Supply Chain Services, hvor virksomheder kan købe sig ind i Amazons egne fragtløsninger. Men hvor alvorlig er truslen mod DSV egentlig? Til sidst vender vi Novos Mike Doustdar, der som en anden tryllekunstner viser Wegovy-pillen frem på LinkedIn. Pillen spiller nemlig en hovedrolle i de 29,5 milliarder kroner i overskud, Novo netop har leveret i første kvartal. Men er det nok til at vende skuden? Vært: Ulrik Rosenkvist Schultz. Fast gæst: Sune Aagaard. Redigering: Buster Hoff.

In dieser Folge haben wir Kerstin Vogel, ehemalige Spitzenschwimmerin und heutige DSV-angestellte Trainingswissenschaftlerin am Bundesstützpunkt Essen, am Mikrofon. Gemeinsam mit Host Lukas Mundelsee spricht Kerstin darüber, wie sie ihre eigenen leistungsportlichen Erfahrungen gewinnbringend an heutige Athletinnen und Athleten weitergibt, welche stilistischen Fehlerbilder ihr immer wieder unterkommen und wie sie daran in Kooperation mit den Trainerinnen und Trainern sowie den Sportlerinnen und Sportlern arbeitet.

Dels ift. dagens aktuelle regnskab fra DSV, og dels ift. et investeringstema, der bevæger sig helt ud i rummet. Hør, hvordan Danske Banks investeringsstrateg, Lars Skovgaard, forholder sig til rumfartsaktierne, når han udfolder og vurderer risici og muligheder for investeringstemaet. I studiet leverer HC Andersen Capitals aktiechef, Michael Friis Jørgensen, sit syn på forsikringsbrancen efter gårsdagens opsigtsvækkende dom om erhvervsskadeerstatning. Vært: Bodil Johanne GantzelSee omnystudio.com/listener for privacy information.

Un estudio de Ricoh revela que el 37% de los empleados en España teme ser sustituido por la inteligencia artificial, en un contexto donde la sobrecarga administrativa sigue afectando al bienestar y la retención del talento. Los trabajadores pierden hasta 16 horas semanales en tareas poco productivas y un 17% se ha planteado dejar su empleo por este motivo, evidenciando el impacto combinado de la automatización y la gestión ineficiente del trabajo.DSV ha lanzado en España su línea DSV Hotels & Logistics, enfocada en optimizar la cadena de suministro del sector hotelero en aperturas, renovaciones y operativa diaria. La propuesta centraliza procesos logísticos y añade seguimiento en tiempo real para mejorar la coordinación, la visibilidad y la eficiencia en un entorno cada vez más complejo.Asociación Empresarial Hotelera de Madrid (AEHM) ha lanzado un nuevo mapa turístico de Madrid, disponible en exclusiva en más de 330 hoteles asociados, con el objetivo de mejorar la orientación y la experiencia del visitante en la ciudad. El material, diseñado junto a Walk Around, combina funcionalidad e ilustración e incluye referencias culturales, gastronómicas y de ocio de la capital.Transavia ha inaugurado una nueva ruta entre Sevilla y Marsella, con dos frecuencias semanales desde el 9 de abril, dentro de su estrategia de refuerzo de la conectividad internacional desde mercados regionales.Suncruise ha participado en Seatrade Cruise Global en Miami para reforzar la proyección internacional de los puertos andaluces, manteniendo reuniones con navieras y operadores de cara a nuevas escalas. En el encuentro se han presentado proyectos de infraestructuras en puertos como Sevilla y Almería, en el marco de una estrategia de crecimiento del turismo de cruceros en Andalucía.

#349: Happy Monday!

In this episode, we dive into the U.S. Postal Service's aggressive strategy to increase postage rates and preserve cash amid a severe financial crisis. This latest price hike is a core component of their ten-year turnaround plan to plug a bleeding balance sheet and maintain liquidity. Next, we discuss how a catastrophic global network strike was averted after FedEx and its pilots reached a tentative labor agreement. Following five years of grueling negotiations, the new contract promises significant pay increases and enhanced pension plans for over 5,000 flight crew members. Finally, we examine the ongoing warehousing reset by looking at the sudden loss of hundreds of logistics jobs at a massive Texas distribution center. Global logistics giant DSV is completely exiting a dedicated contract at its Wilmer facility, highlighting the intense margin pressures currently facing third-party logistics providers. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we dive into the U.S. Postal Service's aggressive strategy to increase postage rates and preserve cash amid a severe financial crisis. This latest price hike is a core component of their ten-year turnaround plan to plug a bleeding balance sheet and maintain liquidity. Next, we discuss how a catastrophic global network strike was averted after FedEx and its pilots reached a tentative labor agreement. Following five years of grueling negotiations, the new contract promises significant pay increases and enhanced pension plans for over 5,000 flight crew members. Finally, we examine the ongoing warehousing reset by looking at the sudden loss of hundreds of logistics jobs at a massive Texas distribution center. Global logistics giant DSV is completely exiting a dedicated contract at its Wilmer facility, highlighting the intense margin pressures currently facing third-party logistics providers. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

#346: Over and out! ❌ Der Weltcup ist vorbei und wir sind in dieser Extrarunde endgültig am Limit

#343: Rösch und Ron sind zurück!

Vi diskuterer tidens hotteste økonomiske spørgsmål og varmeste politiske kartoffel: Boligskat. Lars Løkke ønsker skat på boliggevinster, men hvad er egentlig de økonomiske argumenter? Vi kigger også på en børsmeddelelse fra et amerikansk karaokeselskab, der fik aktiemarkedet til at gå lettere i panik og gav verdens logistikselskaber en på tuden – inklusiv danske DSV. Virksomheden, der indtil for ganske nylig mest af alt lavede karaokeanlæg, har nemlig lavet en ny kunstig intelligens-software, der kan bruges af logistikselskaber. Til sidst diskuterer vi Tyskland, hvor friske tal fra industrien viser solide ordrebøger, hvilket tyder på pæn vækst. Det er fremragende for markedet og Europa, lyder vurderingen. I studiet: Magnus Barsøe og Mikael Milhøj.See omnystudio.com/listener for privacy information.

Olympia-Fazit Atle Lie McGrath liefert viellecht das eine Bild dieser Winterspiele, Federica Brignone entlarvt die Skriptschreiber der FIS. Wir ziehen ein Fazit zum Abschneiden des DSV, des ÖSV und von Swiss Ski.Dieser Podcast wird vermarktet von der Podcastbude.www.podcastbu.de - Full-Service-Podcast-Agentur - Konzeption, Produktion, Vermarktung, Distribution und Hosting.Du möchtest deinen Podcast auch kostenlos hosten und damit Geld verdienen?Dann schaue auf www.kostenlos-hosten.de und informiere dich.Dort erhältst du alle Informationen zu unseren kostenlosen Podcast-Hosting-Angeboten. kostenlos-hosten.de ist ein Produkt der Podcastbude.

In der heutigen Folge sprechen die Finanzjournalisten Anja Ettel und Lea Oetjen über einen neuen Europa-Infrastruktur-ETF, ein neues Sparprogramm bei VW und SpaceX als Rüstungskonzern. Außerdem geht es um Hapag-Lloyd, Flatexdegiro, Heidelberg Materials, Hochtief, ACS, Gold, Silber, Bitcoin, Nintendo, Hasbro, Konami, VanEck Video Gaming and eSports UCITS ETF (WKN: A2PLDF), First Trust Indxx Europe Infrastructure UCITS ETF (WKN: A420NU), Global X European Infrastructure Development UCITS ETF (WKN: A40E7B), Iberdrola, Schneider Electric, Eaton Corp, Airbus, Enel, CRH, National Grid, Vinci, DSV und Compagnie de Saint-Gobain. Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Der Börsen-Podcast Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Efter betydelige udsving i aktier fra både software- og kapitalforvaltningsselskaber var det i torsdag transportsektoren, der fik fortællingen om AI’s fortræffeligheder at føle. Det gik bl.a. ud over den danske logistikkæmpe DSV, men giver det overhovedet mening, at aktien skulle 10% lavere, fordi nogen mener, kunstig intelligens vil ændre branchen fundamentalt? Millionærklubben ser på sagen og svarer desuden på spørgsmål fra lytterne med chefanalytiker Lau Svenssen fra Svenssen & Tudborg samt teknisk analytiker Lars Persson fra Aktierådet i studiet. Vært: Bodil Johanne GantzelSee omnystudio.com/listener for privacy information.

In der heutigen Folge sprechen die Finanzjournalisten Philipp Vetter und Holger Zschäpitz über über KI-Panik in der Logistik-Branche, Sensationszahlen für Arista Networks und Silberstreif für Coinbase. Außerdem geht es um CBRE, C.H. Robinson, Expeditors, DSV, Kühne & Nagel, Algorhythm Holdings, Microsoft, Alphabet, Amazon, Cisco, Apple, Lenovo, Applied Materials, Pinterest, Draftkings, Fastly, Heidelberg Materials, Holcim, Deutsche Telekom, T-Mobile US, Mercedes-Benz, Uber, Tesla, BYD, Nio, Xiaomi, Hermes, LVMH und NXP. Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Der Börsen-Podcast Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Ready for an inspiring career boost? In this episode of Count Me In, Adam Larson sits down with Isabelle Massaad, CFO at DSV, a global leader in transport and logistics. Isabelle's story is anything but ordinary: she began her professional journey at just 17, driven by curiosity, grit, and a knack for finding patterns in chaos. This conversation is full of practical advice and real stories—how Isabelle earned respect, led diverse teams, and transformed financial data into compelling business narratives. She opens up about what it means to be a transformative leader, the essential role of empathy in her leadership style, and why staying adaptable is crucial with so much rapid change in tech and AI. Whether you're just starting out or dreaming of the C-suite, you'll find plenty of actionable takeaways on building soft skills, networking, and embracing lifelong learning. Dive in for an engaging chat and get a fresh take on the future of finance, leadership, and the constantly evolving world of transport and logistics. ___________________________________________________________BILL is a leading financial operations platform for startups to established brands. Headquartered in San Jose, California, we're a trusted partner of leading US financial institutions, accounting firms, and accounting software providers. We empower business owners, CFOs, controllers, and accountants to save time and take control of their payables, receivables, spend, and expense management. For more information, visit bill.com.

Regnskabssæsonen trådte for alvor i karakter denne uge og har stjålet alle overskrifter siden tirsdag. Og et regnskab har fyldt mere end alle andre - Novo. Med sine dårlige udsigter for 2026 og konkurrenten Eli Lilys helt anderledes positive, ser det dystert ud for Novo, som samtidig står over for en række konkrete udfordringer de næste år. Hør hvad Bundlinjens paneldeltagere mener om Novos måde at tackle udfordringerne på og hør hvad de mener om DSV's enorme magtdemonstration på fragtområdet, hvorfor Mærsk ikke har præsteret og hvad Carlsberg skal gøre for at komme over ølkrisen. Ugens panel: Søren Linding, erhvervskommentator og debatredaktør på Finans Niels Lunde, erhvervskommentator på Finans Heidi Birgitte Nielsen, økonomisk redaktør på Finans Vært: Mads Ring Producer: Kasper Søegaard Grafik: Anders ThykierSee omnystudio.com/listener for privacy information.

Et tidligt fremlagt regnskab fra danskernes tidligere darling, Novo Nordisk, sendte aktien lige lukt i hullet på børsen i USA tirsdag. Men var det berettiget eller en overreaktion på et regnskab, som muligvis viser kortsigtede udfordringer, men også bliver tillagt potentiale på langt sigt fra flere analytikere? Millionærklubben tjekker tallene i studiet sammen med aktiechef i HC Andersen Capital, Michael Friis Jørgensen, og direktør i og stifter af Global Health Invest, Claus Johansen. Med på en telefon er også CEO i D/S Norden, Jan Rindbo, om dagens aktuelle regnskabstal, og senior aktieanalytiker i Jyske Bank, Haider Anjum, giver sit besyv med om DSV. Vært: Bodil Johanne GantzelSee omnystudio.com/listener for privacy information.

DSV tog nok engang fusen på alle. Ikke fordi den præsenterede et kæmpe løft i omsætningen. Nej, overraskelsen skal findes et andet sted. Integrationen af konkurrenten logistik-giganten DB Schenker går nemlig så rasende stærkt, at man forventer alt er på plads inden klokken slår 2027. Det er to år før oprindeligt planlagt og er helt uhørt i de størrelsesforhold. Hør i denne Lyn-analyse, hvor stor en operation DSV har formået at sætte turbo, og hvad der gør selskabet second to none i den disciplin. Gæst: Kresten Andersen, journalist, Finans. Vært: Mads Ring. Foto: Thomas Lekfeldt.See omnystudio.com/listener for privacy information.

Find out where the human edge and automation matter in freight brokerage in this episode with Alex Schick of Alliance Logistix, sharing how smart freight automation, carrier invoicing automation, and AI in logistics can drive efficiency without sacrificing carrier relationships or customer trust! Alex talk through why automating tedious tasks like carrier invoicing and document collection speeds payment cycles, reduces errors, and boosts carrier satisfaction, while keeping real humans front and center for load booking and relationship management, why AI should act as an assistant (not a replacement) for brokers, fleets, and driver managers, how tailored transportation technology beats bloated all-in-one systems, and why small fleets can dramatically improve profitability through better cost management, insurance shopping, and maintenance strategies.  If you're a freight broker, fleet owner, or logistics leader trying to balance automation with personal service while protecting margins in a tight market, this conversation is full of practical insights you can apply immediately!   About Alex Schick Alex embarked on his logistics adventure in 2007 at Intransit, which later transformed into DSV Road through several acquisitions. Starting from the bottom in tracking and tracing, he quickly climbed the ranks. By 2009, he was overseeing a small region of freight in Northern California. His responsibilities expanded significantly by 2014, when he began handling all customer bids, pricing, and rate structures for carriers. At that point, he also managed the operations side of about 40% of the branch's freight. In early 2017, he became the branch manager, overseeing the overall strategy and direction of the entire branch. From 2014 to the end of 2018, they grew from 7,000 truckloads per year to 55,000 truckloads per year as an agency. They expanded from 6 employees to 18 and from $5 million in revenue to almost $20 million. They were a bootstrapped company growing like crazy before the tech boom hit the industry. He left DSV in 2019 and joined Wilson Logistics in 2020. Together with his wife, Natalie, they started to rebuild. By the end of 2020, they had hired their first employee in their Wilson Agency. In early 2023, they departed the agent model and co-founded Alliance Logistix LLC. By late 2023, they ventured into asset-based trucking alongside their brokerage. Today, they have 12 employees in the brokerage, 7 drivers, and 2 employees at their asset company. They are currently handling volumes of more than 30,000 truckloads annually. With nearly two decades of industry experience, he's committed to fostering strong relationships, building alliances, and identifying synergies with their customers and carriers. They are focused on being a lean as possible, efficient as possible, and eliminating and/or automating repetitive and monotonous tasks with technology and automation. The most important thing is that they are keeping to their roots of the family feel for their customers, carriers, and employees alike. After all, in logistics, it's all about the journey and the company you keep!   Connect with Alex Website: https://www.alliancelogistix.com/  Email: aschick@alliancelogistix.com  Office Phone: 559-899-3177  

In this episode of the FreightWaves Morning Minute, we examine the escalating standoff between federal regulators and California that could paralyze the nation's largest trucking workforce. Transportation Secretary Sean Duffy is threatening to strip the state of its commercial licensing power over safety concerns regarding 17,000 non-domiciled licenses. Markets are also bracing for a major shift, with analysts predicting a dramatic spike in logistics mergers and acquisitions by the end of 2026,. Investors are increasingly favoring specialized tech platforms over generic brokerages, exemplified by Echo Global Logistics' recent move to acquire ITS Logistics. Finally, the nearshoring boom is driving tangible infrastructure investments along the southern border. DSV has broken ground on a massive regional headquarters in Arizona to support growing cross-border trade, joining other industry giants in expanding their footprint to capture Mexico-bound freight. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of the FreightWaves Morning Minute, we examine the escalating standoff between federal regulators and California that could paralyze the nation's largest trucking workforce. Transportation Secretary Sean Duffy is threatening to strip the state of its commercial licensing power over safety concerns regarding 17,000 non-domiciled licenses. Markets are also bracing for a major shift, with analysts predicting a dramatic spike in logistics mergers and acquisitions by the end of 2026,. Investors are increasingly favoring specialized tech platforms over generic brokerages, exemplified by Echo Global Logistics' recent move to acquire ITS Logistics. Finally, the nearshoring boom is driving tangible infrastructure investments along the southern border. DSV has broken ground on a massive regional headquarters in Arizona to support growing cross-border trade, joining other industry giants in expanding their footprint to capture Mexico-bound freight. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of the Daily, FreightWaves investigates a startling report on national security, detailing Dragon in the Cab: How China Quietly Embedded Itself in American Trucking. We discuss the growing concerns regarding unvetted drivers hauling military freight and the specific vulnerabilities found within U.S. port infrastructure. In major corporate news, we cover the return of a legacy fleet to private domestic ownership as Former, current USA Truck execs acquire TL carrier from DSV. Conversely, we analyze a severe financial crisis in the last-mile sector, looking at the factors behind Last mile provider FAST Group's post-merger meltdown. The industry's financial volatility is further highlighted by the news that STG Logistics files Chapter 11, charts path forward. We also touch on regulatory and labor enforcement, including a whistleblower victory where a Texas carrier ordered to pay more than $100K to fired driver and a security-related contract cancellation where USPS insourcing forces Denver contractor to layoff 700 workers. Finally, we examine critical economic indicators showing a shift in supply chain strategies as Warehouses empty in December. This segment explores how historic lows in inventory levels may force shippers back into a reactive, just-in-time operating environment for the year ahead. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of the Daily, FreightWaves investigates a startling report on national security, detailing Dragon in the Cab: How China Quietly Embedded Itself in American Trucking. We discuss the growing concerns regarding unvetted drivers hauling military freight and the specific vulnerabilities found within U.S. port infrastructure. In major corporate news, we cover the return of a legacy fleet to private domestic ownership as Former, current USA Truck execs acquire TL carrier from DSV. Conversely, we analyze a severe financial crisis in the last-mile sector, looking at the factors behind Last mile provider FAST Group's post-merger meltdown. The industry's financial volatility is further highlighted by the news that STG Logistics files Chapter 11, charts path forward. We also touch on regulatory and labor enforcement, including a whistleblower victory where a Texas carrier ordered to pay more than $100K to fired driver and a security-related contract cancellation where USPS insourcing forces Denver contractor to layoff 700 workers. Finally, we examine critical economic indicators showing a shift in supply chain strategies as Warehouses empty in December. This segment explores how historic lows in inventory levels may force shippers back into a reactive, just-in-time operating environment for the year ahead. Follow the FreightWaves NOW Podcast Other FreightWaves Shows Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Supply Chain Connections, Greg Slawson joins Brian Glick to share insights from a career spanning automotive manufacturing, global consulting, logistics tech startups, and leading freight forwarders. The conversation dives deep into how large organizations approach decision-making, how to handle cultural differences in global logistics, and what the future holds for technology in the industry.Topics covered include: The evolution of Greg's supply chain journey from Ford to Deloitte to DSV Lessons on navigating bureaucracy, change management, and cross-cultural communication The challenge of balancing customer needs with asset utilization in large carriers The emerging role of agentic AI and orchestration in reducing manual, low-value tasks Practical AI applications for 3PLs to boost efficiency and profitability Why real partnerships between shippers and service providers are rare—but powerful when they happen The ongoing shift toward more volatile, opportunity-rich global supply chainsAbout the Guest: Greg Slawson brings over 35 years of supply chain and logistics leadership spanning automotive, technology, and consulting sectors. His career includes senior roles at Ford Motor Company in supply chain and logistics operations, followed by executive positions as VP at G-Log/Oracle, CEO at OPS, and EVP Vertical Lead at DSV, one of the world's largest logistics providers. Throughout his career, Greg has driven operational excellence and strategic transformation across complex global supply chains.Connect with GregConnect with BrianFollow Chain.io on LinkedIn

#326: Östersund startet wild ❄️

Auch im zweiten Teil meines Gesprächs mit Jan Pommer, dem Leiter Leistungssport im Deutschen Schwimm-Verband (DSV), geht es um die Zukunft unserer Sportart und was es für eine Wiederbelebung des Wasserballs braucht. Wir reden darüber, wie Wasserball wieder mehr Aufmerksamkeit bekommen kann – durch verständlichere Regeln, neue Formate und eine stärkere Präsenz in den Medien. Dabei insbesondere welche Bemühungen der DSV hierzu seit geraumer Zeit unternimmt und welches Ziel damit verbunden ist. Jan erklärt ebenfalls, warum Nachwuchsförderung und klare Strukturen entscheidend sind, um langfristig erfolgreich zu sein – und weshalb Fairplay und Haltung wichtiger bleiben als jeder schnelle Sieg. Ein offenes Gespräch über Wandel, Verantwortung und die Leidenschaft, Wasserball gemeinsam in eine bessere Zukunft zu führen. ➡️ Jetzt reinhören, teilen und weitersagen – denn die Zukunft unseres Sports beginnt mit uns!

#320: Et voilà!

Transport Topics is the news leader in trucking and freight transportation. Today's briefing covers DOT's reaction to another fatal crash involving an immigrant truck driver, DSV's efforts to part with USA Truck and Rivian agreeing to settle a lawsuit. Learn more about your ad choices. Visit podcastchoices.com/adchoices

De Konservative og SVM-regeringen har indgået en aftale om næste års finanslov. EU lover at hjælpe Ukraine økonomisk de næste to år. Transportkæmpen DSV skal finde andre veje til at øge værdien. Nye nuancer i Novos bestyrelsesdrama. ATP hæver pensioner med 2 pct. HK-formand stopper. Flying Tiger lukker flere danske butikker.   Vært: Trine Duvander (trine.duvander@borsen.dk)

Regnskabssæsonen er i fuld gang og herhjemme har DSV netop leveret ugens vigtigste tal. Samtidig er der gang i geopolitikken, hvor amerikanerne har indført nye oliesanktioner mod Rusland. Det sender olieprisen op. I dagens panel tager aktieanalytiker og rådgiver for Regulær Invest Nordiske Aktier Michael West Hybholt samt chefanalytiker i Svenssen & Tudborg temperaturen på markedet og regnskaberne. Hybholt fortæller også om de seneste indkøb til porteføljen, og hvorfor han mener, at Mærsk-aktien potentielt kan blive afnoteret. Vært: Adam Geil See omnystudio.com/listener for privacy information.

I dag offentliggøres amerikanske jobtal for august. Trumps toldsatser er muligvis ikke lovlige. Handel­s­af­talen mellem Trump og EU er tvivlsom. Det er ikke sikkert, at alle i fredsstyrken er klar til at indsætte tropper. Ørsteds fem centrale angreb mod Trump-regeringen. For elbilkæmpe fra Kina står proble­merne i kø. Over 10.000 DSV-medarbejdere kan blive fyret efter opkøb. Så meget sparer familier på afgifter og skatte­let­telser.   Vært: Trine Duvander (trine.duvander@borsen.dk)

Discover how Aurora and McLeod Software have partnered to integrate autonomous trucks directly with mainstream Transportation Management Systems (TMS), marking an industry first. This "plug-and-play" integration aims to streamline autonomous vehicle adoption for mid-sized fleets of 100 to 1,000 trucks, supporting vital functions like scheduling and invoicing, with a full rollout planned for 2026 to McLeod's 1,200+ customers.  On the infrastructure front, convenience store giant Wawa has entered the travel center business, opening its first facility in Hope Mills, North Carolina, offering 18 free parking spots and high-speed diesel lanes. While primarily targeting short-haul drivers and mid-trip stops without offering showers, this initiative highlights the critical and ongoing need for more truck parking capacity.  In air freight, strategic partnerships are expanding capacity and control, with Atlas Air providing dedicated Boeing 777 freighters to Etihad Airways and DSV for key routes. These moves offer major players greater command over their capacity and schedules in complex supply chains, emphasizing reliability and logistics ownership.  Furthermore, Flexport has teamed up with BlackRock to offer up to $250 million in supply chain financing, including term loans, asset-based lines of credit, and crucial tariff financing. This initiative provides businesses with vital working capital to bridge the gap between inventory purchase and sales, directly addressing current trade risks and evolving supply chain financing needs.  On the trade policy front, Mexico is proposing a 50% tariff on a range of Chinese imports for its 2026 budget, aiming to protect domestic manufacturers and address U.S. concerns about Chinese goods entering via Mexico. Additionally, Mexico has implemented an immediate ban on finished footwear imports and suspended small-package shipments to the U.S., impacting e-commerce platforms as the U.S. ends its de minimis tariff exemption for low-value imports.  Finally, U.S. container ports saw a temporary jump in July volume due to front-loading ahead of potential new tariffs, but the overall trend reveals a six-month downtrend, with a projected 5.6% year-over-year decrease in total 2025 import volume. This unusual decline suggests a major strategic realignment, with some inbound volume diverting to other North American ports outside the U.S..  Learn more about your ad choices. Visit megaphone.fm/adchoices

In der heutigen Folge sprechen die Finanzjournalisten Anja Ettel und Philipp Vetter über schlechte Zeiten für Bank-Aktien, eine luxuriöse Hoffnung und einen neuen Rüstungs-ETF mit Patent-Bonus. Außerdem geht es um Commerzbank, Deutsche Bank, Delivery Hero, Richemont, Swatch, LVMH, Kering, Alphabet, Amazon, Microsoft, Meta, Xtrackers Europe Defence Technologies ETF (WKN: DBX0W8), Rheinmetall, BAE Systems, Safran, Airbus, Rolls Royce, DSV, VanEck Defense ETF (WKN: A3D9M1), Lockheed Martin, Raytheon, Northrop Grumman, SPDR S&P Europe Defense Vision ETF (WKN: A417ZR), Leonardo, Saab, Thales und Aroundtown. Die Tickets zum Finance Summit am 17. September bekommt ihr 40 Euro günstiger – aber nur mit dem exklusiven Code AAA2025, der ihr unter dem folgenden Link eingeben müsst: https://veranstaltung.businessinsider.de/BN5aLV Außerdem könnt ihr unter diesem Link euer Depot hochladen – und mit etwas Glück wird kein Geringerer als Christian W. Röhl euer Depot beim Summit checken und optimieren. https://form.jotform.com/Product_Unit/formular-finance-summit-depot-check Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts und AAA-Newsletter. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Der Börsen-Podcast Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Flotte regnskaber fra de amerikanske tech-giganter, Meta og Microsoft, efter lukketid i går. Millionærklubben dissekerer udmeldingerne og debatterer, hvor holdbare de stærke aktiekursstigninger er, sammen med Saxo Banks investeringsstrateg, Oskar Barner Bernhardtsen. I studiet giver chefanalytiker Lau Svenssen sit besyv med om dagens regnskab fra DSV og gårsdsdagens udmelding fra Teekay Tankers samt samler op på rentemødet i USA. Vært: Bodil Johanne GantzelSee omnystudio.com/listener for privacy information.