絞り込み結果
19 件並び順
1:50:48ENWhy Inference Matters 12× More Than TrainingFrançois Chaubard, Dan Fu, Jeff Morgan, Joe Fioti, Liangsheng Yin · Y CombinatorYC Paper Club walks the inference stack from an API request down to GPU kernels. Four talks from Together AI, Ollama, Luminal, and SGLang argue that serving quality, not just training FLOPs, is where most of the compute and the money now sit.
38:14ENDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom Siliconホスト Sarah Guo, Elad GilThe Stanford diffusion pioneer tells No Priors why a 50-person lab is training language models that generate many tokens at once — and why that maps to GPUs the way transformers once mapped to training.
1:10:15ENThe 100x lives between the layers: Dylan Patel on co-design and a 60x yearSemiAnalysis’s founder argues that matching models, kernels, and silicon is what turns a 2x here and a 2x there into 100x, while equivalent-quality inference has gotten about 60 times cheaper in a year.
59:06ENInference Costs Fell 100x. Tri Dao Says There's Another 10x — and NVIDIA's 90% Won't Holdホスト Jacob EffronThe FlashAttention author and Together AI chief scientist on why hardware portability is a myth, how Claude Code made him 1.5x faster, and why workloads will split the chip market.
2:36:13ZH松琳解读 DeepSeek、Kimi 与 MiniMax 的注意力新论文ゲスト Songlin Yang(松琳) · ホスト 张小珺从动态稀疏注意力到线性注意力混合架构:三条长上下文路线如何兼顾训练、推理与硬件效率
1:03:21ENHorace He on Building ML Systems for Trillion-Trillion OperationsHorace He · MetaHow GPU bottlenecks, compiler design and distributed parallelism shape the systems behind large-scale model training.
46:35ENThey trained a real-time video model in 20 hours and skipped the game engineDecart CEO Dean Leitersdorf tells Sequoia how Oasis runs live on H100s, why ChatGPT is a fallen wall rather than a pain point, and why the next trillion-dollar company may be a generated experience.
36:49ENJensen Huang: NVIDIA Is No Longer Building Computers. It Is Building AI Factoriesホスト Sarah Guo, Elad GilOn No Priors, NVIDIA’s CEO walks through hyper Moore’s law, NVLink, a 100,000-GPU cluster stood up in weeks, and why tokens are the new commodity.
39:18ENOpen and Closed Will Tie at the Same Size: PyTorch's Former Lead on Fast InferenceA six-month library swap at Meta became a five-year rebuild. Lin Qiao wants the rest of the industry to get that stack in five weeks.
1:52:18ENTwo Weeks, Thirty Compiles: How Adam Majmudar Wrote a GPU From Scratchホスト Nathan LabenzA Cognitive Revolution walkthrough of TinyGPU, from SIMD kernels and memory latency to an open-source chip flow that runs on a laptop.
1:42:17ENThe Cheapest Inference Provider Is the Most Dangerousホスト Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.
38:33ENSpeed Is the Thing You Can Buy: Baseten’s Bet on Inferenceホスト Sarah Guo, Elad GilTuhin Srivastava tells Sarah Guo and Elad Gil why a six-person chatbot company running a billion tokens a day should not be building its own serving stack.
2:30:06ENTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home Turfホスト Nathan LabenzIn a solo emergency episode, Nathan Labenz argues that Albert Gu and Tri Dao's selective state space model is the first architecture since 2017 that deserves to share the Transformer’s throne.
3:08:46ENWho owns the off switch? George Hotz on tinygrad, open-source AI, and Godゲスト George Hotz · ホスト Lex FridmanOn his third Lex Fridman podcast, the comma.ai and tiny corp founder argues that compute, intelligence, and the off switch should live in people's houses, not in a trusted few.
3:34:03ENPython Isn't Finished: Chris Lattner on Mojo and the Hardware Messゲスト Chris Lattner · ホスト Lex FridmanThe LLVM creator returns to Lex Fridman to argue that Python should grow C-like performance instead of forcing people out of the language.
52:11ENJensen Huang: For 25 Years, 99 Percent Were Right. NVIDIA Bet on the 1 Percentホスト Sarah Guo, Elad GilThe NVIDIA CEO tells No Priors how CUDA survived a decade without customers, why the company tries to stay lazy, and what he wants built for drugs and climate.
1:44:11ENA Single GPU Is Enough: Jeremy Howard on Teaching Deep Learningゲスト Jeremy Howard · ホスト Lex FridmanThe fast.ai founder tells Lex Fridman why domain experts, not more papers or more TPUs, will decide whether deep learning actually helps people.
1:01:23ENJensen Huang on AI Factories, Digital Workers and Why the Build-Out Has Barely BegunNvidia's CEO tells a room of institutional investors how accelerated computing led to AI factories, and where agentic AI, robotics and sovereign AI go next.
17:19ENFrom Atoms to Data Centers: How a Modern Processor Finds Parallelism in a Serial Programゲスト Jim Keller · ホスト Lex FridmanJim Keller walks through the layers of abstraction in computer design, from transistors to out-of-order execution, branch prediction and deterministic answers.








