筛选结果
30 篇排序
1:25:33EN → 简体中文GPU 产能为什么是三年一块的矩形 —— Erik Bernhardsson,Modal嘉宾 Erik Bernhardsson · 主持 Vibhu SapraModal 的 CEO 在 Latent Space 上解释:AI 需求几乎从来都不长成一份三年 GPU 合同的样子,公司如何把算力在数千客户之间池化,以及沙箱如何从差点被砍掉的产品长成了三分之一的收入。
35:39ENCompute Scaled a Millionfold. Bandwidth Only 40x. Fractile Wants to Fix That Ratio主持 Sarah Guo, Elad GilIn the summer of 2022, Walter Goodwin founded Fractile in London to build very fast inference chips for the world's largest models.
38:14ENDiffusion Will Win Inference: Stefano Ermon Bets Parallel Tokens Beat Custom Silicon主持 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.
37:07ENArm’s 98.5% margin business just started shipping chips主持 Sarah Guo, Elad GilCEO Rene Haas tells No Priors why the IP company built a physical CPU for Meta, and why CPUs still sit at the center of AI systems.
41:05ENThere is no moat: Booking’s CEO on a $1 stock, agentic travel, and why AI still doesn’t move the numbers主持 Sarah Guo, Elad GilGlenn Fogel cashed his 1999 bonus, joined Priceline in February 2000, and watched the Nasdaq peak a week later — then spent 25 years turning a near-delisting into a thousand-fold ride he now wants to spend on AI travel agents.
1:01:27ENTwo Years and Four Months to a Live Reactor: Valar Atomics Wants Energy Ten Times Cheaper主持 Sarah Guo, Elad GilIsaiah Taylor walks Sarah Guo through Ward 250 in Utah and argues that nuclear is a hardware-iteration problem, not a paper-design one.
42:57ENInference Is the Last Market: Baseten’s 30x Year主持 Sarah Guo, Elad GilCEO Tuhin Srivastava on a billion-dollar inference cloud, why the app layer survives, and a GPU crunch worse than the narrative.
1:16:21ENJensen Huang’s Year-End Case That Token Costs Can Fall a Billion-Fold Without Killing Jobs主持 Sarah Guo, Elad GilOn No Priors, NVIDIA’s CEO uses radiology, open source, and a $2 trillion R&D budget to argue that 2025’s extremes missed the stack.
31:57ENPeak 700 GW, average 300 GW: Base Power is wiring home batteries into a power plant主持 Sarah Guo, Elad GilZach Dell tells Elad Gil how a $1.3 billion raise, a Texas-only retail business, and a Tesla-and-SpaceX hiring bench are meant to cut the cost of moving an electron.
47:18ENThe Analyst Tracking 200 GPU Clouds Breaks Nvidia Into a Three-Headed Dragon主持 Sarah Guo, Elad GilSemiAnalysis founder Dylan Patel on OpenAI’s open-weight model, a coming neocloud wipeout, and why a chip startup has to beat Nvidia on hardware, networking, and software at once.
50:35ENCopilot Already Writes Half the Code. GitHub's CEO Says Agents Still Have Four Gates to Clear主持 Sarah Guo, Elad GilThomas Dohmke on agent mode, why median developers are not going anywhere, and how AI is repricing the tools that write software.
22:32ENThe $5.5 Million Was Only the Last Mile主持 Sarah Guo, Elad GilOn No Priors' 100th episode, Sarah Guo and Elad Gil separate DeepSeek's paper cost from the compute behind it, then walk through OpenAI's Deep Research, Stargate, and their bets for 2025.
23:37ENA Hundred GPUs in Seconds: Why Modal Threw Out Docker and Kubernetes主持 Sarah Guo, Elad GilErik Bernhardsson tells Elad Gil that AI inference is too bursty for year-long GPU contracts, and that the cloud should feel as fast as a laptop.
42:21ENThey Cooled 5,000 Watts per Square Centimeter in Space. Now Diamond Goes on AI Chips主持 Sarah Guo, Elad GilAkash Systems founders Felix Ejeckam and Ty Mitchell tell Sarah Guo why heat is the real limiter on AI, and how lab-grown diamond bonded to semiconductors is their answer.
36:49ENJensen Huang: NVIDIA Is No Longer Building Computers. It Is Building AI Factories主持 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.
42:42ENEthereum Once Held Two Thousand GPT-3 Clusters of GPUs主持 Sarah Guo, Elad GilFoundry CEO Jared Quincy Davis on idle H100s, why today's AI cloud is still colocation, and how compound systems move work off giant interconnected clusters.
36:20ENA Hundred Million Tokens in Context Would Cost $10 Million. Tengyu Ma Runs the RAG Math主持 Sarah Guo, Elad GilThe Stanford professor and Voyage AI founder argues that retrieval, not a giant context window, is how enterprises should manage private knowledge.
29:09ENThe Hyperscalers Are Spending $200 Billion — and the Next Bottleneck Is a Power Plant主持 Sarah Guo, Elad GilSarah Guo and Elad Gil walk through music models, on-device LLMs, long context, and why AI capex now sits beside oil and broadband.
26:03ENGPT-4-Class Models Will Be Cheap. The Frontier Will Still Need a Patron.主持 Sarah Guo, Elad GilSarah Guo and Elad Gil walk through Mosaic’s Law, Microsoft’s Inflection hire, where the real money sits, and why the first AI wave looks consumer.
38:33ENSpeed Is the Thing You Can Buy: Baseten’s Bet on Inference主持 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.
39:03ENMI300 Leads Inference by 2x: AMD's CTO on Winning Chip Competition Without a Walled Garden主持 Sarah Guo, Elad GilWhen Mark Papermaster became AMD's CTO in fall 2011, nearly all of the company's revenue sat on PCs and gaming, Moore's Law was slowing, and everyone knew it needed a turnaround.
30:03ENA Dinner-Plate Chip Cut Training From 60 Days to 3.5主持 Sarah Guo, Elad GilCerebras CEO Andrew Feldman tells No Priors why wafer-scale silicon, not another GPU, is the answer to the compute crunch — and why most production models will stay at 13 billion parameters or below.
3:34:03ENPython Isn't Finished: Chris Lattner on Mojo and the Hardware Mess嘉宾 Chris Lattner · 主持 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.
40:25ENA 6-Billion-Parameter Clone of ChatGPT’s Core Trick主持 Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.
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