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The Inference Engineering Masterclass: 10x faster models, quantization, speculative decoding, Rubin, & self-optimizing AI Baseten Philip Kiely and ali explain what actually happens after a model is trained, why turning weights into a fast and reliable product creates an entirely new optimization problem, how quantization errors can cancel out... show more

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149,249 次观看 • 3 个月前 •via X (Twitter)

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John3 个月前

speculative decoding was the one that surprised me most in practice. the draft model rejection rate changes a lot based on prompt style, so batching similar prompt types together before running speculative decode gave a much bigger throughput lift than just tuning the draft model itself

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Aayaan Naqvi3 个月前

@baseten @philipkiely @waterloo_intern woahhhhh @waterloo_intern sick

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Jong Hyun Park3 个月前

@baseten @philipkiely @waterloo_intern Is a durable moat possible in inference tech on a multi year timescale?

相关视频

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

What is AI inference engineering, why is it such an in-demand skill, and how do you break into the field? With author of Inference Engineering Philip Kiely and head of training at Baseten Charlie O'Neill 0:00: What is inference? 2:47: History of inference 4:59: Downstream effects of AI research on inference 13:54: What you'll learn from Inference Engineering 16:14: Advice for engineers transitioning into AI 19:00: Open source models driving inference growth 20:55: Specialization vs. frontier closed models 23:51: "Big Token" and the importance of open source AI 27:18: Where to get Inference Engineering

Madison Kanna

119,268 次观看 • 7 个月前

Loading DeepSeek v4 Flash just reminded me how good and fast this model is and how well it runs on DGX Sparks. GLM-5.2 is really awesome, but if you want fast and concurrent inference, DS4F is still a top choice IMO. Created by DS4F in single shot The coffee guy

Loading DeepSeek v4 Flash just reminded me how good and fast this model is and how well it runs on DGX Sparks. GLM-5.2 is really awesome, but if you want fast and concurrent inference, DS4F is still a top choice IMO. Created by DS4F in single shot The coffee guy

Mia

40,899 次观看 • 3 个月前

Today, we're introducing GLM-5.2 Fast: our GLM-5.2 Model API designed for the most demanding real-time use cases. The Fast tier delivers 2-3x higher TPS than the standard GLM-5.2 MAPI.

Today, we're introducing GLM-5.2 Fast: our GLM-5.2 Model API designed for the most demanding real-time use cases. The Fast tier delivers 2-3x higher TPS than the standard GLM-5.2 MAPI.

Baseten

115,573 次观看 • 3 个月前

Long video generation is a systems problem. Introducing LongLive-2.0 from NVIDIA Research: an end-to-end NVFP4 training and inference system for long video generation. Low-precision deployment often relies on post-training quantization, creating a gap between how models are trained and how they run. LongLive-2.0 aligns NVFP4-aware training, distillation, and W4A4 inference, maintaining strong benchmark quality while improving speed and memory efficiency.

Long video generation is a systems problem. Introducing LongLive-2.0 from NVIDIA Research: an end-to-end NVFP4 training and inference system for long video generation. Low-precision deployment often relies on post-training quantization, creating a gap between how models are trained and how they run. LongLive-2.0 aligns NVFP4-aware training, distillation, and W4A4 inference, maintaining strong benchmark quality while improving speed and memory efficiency.

NVIDIA AI

60,715 次观看 • 5 个月前

$NBIS cofounder Roman Chernin describes how their recent acquisitions of Eigen AI and Clarifai were all about speed, incredible talent, and acceleration: "The philosophy is very simple. We need to build so many things, and we need to move so fast, that we're always looking for people who can accelerate us. It should be exceptional talent, and/or something that has a great adoption." "Our two recent acquisitions [were] two teams that work on inference optimization. A big part of our business is how efficiently we convert GPUs into tokens. And these two teams — Eigen AI and Clarifai — one is focused on model optimization, the engine of inference. How you run specific models and all the techniques around spec decoding, quantization, and so on." "And the other is system optimization. All the routing, KV caching, and orchestration across the big cluster of compute and so on." "We have a very strong internal team working on inference. But we felt that we needed to move faster, bring more capabilities. Because the market is so fast."

$NBIS cofounder Roman Chernin describes how their recent acquisitions of Eigen AI and Clarifai were all about speed, incredible talent, and acceleration: "The philosophy is very simple. We need to build so many things, and we need to move so fast, that we're always looking for people who can accelerate us. It should be exceptional talent, and/or something that has a great adoption." "Our two recent acquisitions [were] two teams that work on inference optimization. A big part of our business is how efficiently we convert GPUs into tokens. And these two teams — Eigen AI and Clarifai — one is focused on model optimization, the engine of inference. How you run specific models and all the techniques around spec decoding, quantization, and so on." "And the other is system optimization. All the routing, KV caching, and orchestration across the big cluster of compute and so on." "We have a very strong internal team working on inference. But we felt that we needed to move faster, bring more capabilities. Because the market is so fast."

TBPN

30,676 次观看 • 5 个月前

The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, Mathias Lechner, joins Piotr Mazurek, Piotr Mazurek (in SF 🌉), from our inference team, to discuss what actually limits token throughput and how we're optimizing for it.

The bottleneck in LLM inference isn't compute. It's how fast you can move the weights. Our CTO Mathias Lechner, Mathias Lechner, joins Piotr Mazurek, Piotr Mazurek (in SF 🌉), from our inference team, to discuss what actually limits token throughput and how we're optimizing for it.

Liquid AI

21,393 次观看 • 5 个月前

Sachin Katti says OpenAI had Astra optimize its own inference on NVIDIA's Rubin chip, adding another 2x in 72 hours "It's a testament to NVIDIA's hardware that out of the box we got a 3x improvement in throughput. We literally took the same code that we had optimized for Blackwell, ran it on Rubin, got 3x improvement." "Then, can we actually make Astra run agents using Astra as the base model, go optimize this inference of Astra itself on Rubin? It's a recursive loop. The model is figuring out how to optimize its own inference serving on Rubin chips, and over 72 hours the model produced another 2x improvement on the throughput." "Things that would take weeks, if not months, to optimize in the past, we now are getting models that are very capable of actually producing over a weekend." _______ Link and more from Sachin Katti's conversation:

Sachin Katti says OpenAI had Astra optimize its own inference on NVIDIA's Rubin chip, adding another 2x in 72 hours "It's a testament to NVIDIA's hardware that out of the box we got a 3x improvement in throughput. We literally took the same code that we had optimized for Blackwell, ran it on Rubin, got 3x improvement." "Then, can we actually make Astra run agents using Astra as the base model, go optimize this inference of Astra itself on Rubin? It's a recursive loop. The model is figuring out how to optimize its own inference serving on Rubin chips, and over 72 hours the model produced another 2x improvement on the throughput." "Things that would take weeks, if not months, to optimize in the past, we now are getting models that are very capable of actually producing over a weekend." _______ Link and more from Sachin Katti's conversation:

Fireside Alpha

40,013 次观看 • 1 个月前

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

GLM-5.2 running on Mac Studio Clusters or RTX GPU rigs isn't how Local AI will get mass adopted. For mass adoption we need to focus on two axes: 1. Intelligence/Speed Frontier. This is being tracked on local dot ai. Aggressive co-design is needed across the entire stack to push up the frontier specifically for local hardware. Better kernels, inference tricks like spec decoding, quantizing models, model routing. 2. Usability / UX. To use Local AI today, you need to install CLIs, select a model, a quantization, a harness that works best with that model, and you need to configure your inference engine. It's not very accessible. More work needs to be done on making this seamless, so the user doesn't even know the AI is running locally.

Alex Cheema

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Ray Fernando

10,580 次观看 • 4 个月前

Understanding GPUs will change your AI Engineering journey beyond agents and LLM. Rise of LLMs and deep learning introduced dedicated GPU hardware which serves your models for training and inference. You don't need to start with C++, CUDA, and Maths, but: > Components of a GPU and roles > How a token flows through hardware > Compute vs memory, the two limits > What FLOPS means in practice > Training vs inference hardware needs > What is CUDA Kernels > Calculating GPU memory from model size > Quantization to fit bigger models > Multi-GPU and splitting large models Hugging Face has the feature of calculating FLOps and recommending GPUs for your use case. You must explore. In terms of inference engineering, GPU understanding are much helpful your platform performance and handling multiple requests. Read this article for complete breakdown of GPUs for AI Engineers doesn't require any pre-requisite.

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Karan🧋

33,453 次观看 • 1 个月前

While exploring diffusion language models (DLMs), we kept asking ourselves: given speculative decoding, a lossless acceleration technique, why do we still need DLMs? 🤔 Our latest work, ORL, tries to answer this question: AR optimizes intelligence 👆, speculative decoding optimizes speed 👉, and DLMs optimize the Pareto frontier of speed and intelligence 🚀. Through co-design across the algorithms, the training and inference engine, and the kernels, ORL pushes out a new Pareto frontier of intelligence and speed. Similar performance, much faster than speculative decoding. You say lossy spec? ORL-DLM is better too! 😎 🌐 Project + live demos:

While exploring diffusion language models (DLMs), we kept asking ourselves: given speculative decoding, a lossless acceleration technique, why do we still need DLMs? 🤔 Our latest work, ORL, tries to answer this question: AR optimizes intelligence 👆, speculative decoding optimizes speed 👉, and DLMs optimize the Pareto frontier of speed and intelligence 🚀. Through co-design across the algorithms, the training and inference engine, and the kernels, ORL pushes out a new Pareto frontier of intelligence and speed. Similar performance, much faster than speculative decoding. You say lossy spec? ORL-DLM is better too! 😎 🌐 Project + live demos:

Chenfeng_X

21,292 次观看 • 22 天前

Chinese labs aren't just dropping model weights anymore, they're building full ecosystems. Z.ai's ZCode + GLM 5.2 = native IDE, 1M context, agentic workflows, and now dSpark speculative decoding. The open model platform war is here. 👇

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Boxmining

14,831 次观看 • 4 个月前

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14,138 次观看 • 1 个月前

you can now get unlimited free GLM 5.2 for the first time in history 😳 this is bigger than ever. here's how to do it: 1 / cancel your existing subscriptions: cancel your Lovable, Cursor, and Claude Code subscriptions. you don't need them anymore 2 / sign up for an account on freebuff you can now use unlimited GPT 5.6 and deepseek as well. 3 / to get GLM 5.2: go to the earn tab, and complete the bounties. a bounty provides unlimited GLM 5.2 messages for an hour, each. this isn't going to last forever.

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Victor Cheng

55,165 次观看 • 2 个月前

JUST IN: 🇨🇳 China’s new open-source AI model, GLM-5.2 by Zhipu AI, reportedly matches Anthropic’s Claude Mythos in security bug detection. The model outperformed some Claude versions in independent cybersecurity benchmarks and can achieve Mythos-level performance. Unlike Claude Mythos, GLM-5.2 is open-source and significantly cheaper to run. The gap between US and Chinese AI models is narrowing rapidly.

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63,496 次观看 • 4 个月前

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Rob Hallam

19,906 次观看 • 4 个月前

CHINA IS CATCHING UP IN AI 🇨🇳 China’s new open-source AI model, GLM-5.2 by Zhipu AI, reportedly matches Anthropic’s Claude Mythos in security bug detection. The model outperformed some Claude versions in independent cybersecurity benchmarks and can achieve Mythos-level performance. Unlike Claude Mythos, GLM-5.2 is open-source and significantly cheaper to run. The gap between US and Chinese AI models is narrowing rapidly.

CHINA IS CATCHING UP IN AI 🇨🇳 China’s new open-source AI model, GLM-5.2 by Zhipu AI, reportedly matches Anthropic’s Claude Mythos in security bug detection. The model outperformed some Claude versions in independent cybersecurity benchmarks and can achieve Mythos-level performance. Unlike Claude Mythos, GLM-5.2 is open-source and significantly cheaper to run. The gap between US and Chinese AI models is narrowing rapidly.

CryptoGoos

71,944 次观看 • 4 个月前

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OpenAI's New Agent Stack: Superhuman Computer Use, Dots, Decisions API, UltraFast, & the AI Cloud OpenAI’s Ari Weinstein and Nikunj Handa explain why Computer Use is already becoming faster than humans at real-world tasks, how Dots gives agents their own cloud computers, why better models can now debug and recover from failures, how Decisions API went from idea to product in weeks, and why async tools, UltraFast inference, caching, compaction, and higher-level agent primitives are turning OpenAI’s API into an AI-native cloud.

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David Ondrej

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Alex Finn

125,273 次观看 • 4 个月前