筛选结果
53 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。
1:26:46ENThere Likely Is a Level of Intelligence We Shouldn't Go Past主持 Nathan LabenzNathan Labenz brings notes from The Curve, a chip startup whose token bill beat payroll, and a software industry that now performance-reviews people for Claude slop.
1:04:26ENAt 100,000 Accelerators, Something Fails Every HourGoogle AI infrastructure chief Amin Vahdat tells Sequoia's Sonya Huang why FLOPS is a vanity metric, why the TPU line split in two, and why he would rather wait on a utility than build his own gigawatt.
56:31ENPost-Training Wins Inference: Why Owning the Weights Beats Betting on One Lab主持 Jacob EffronApplied Compute CEO Yash Patil argues that control, private evals, and post-training on the biggest workloads matter more than fearing that frontier labs will turn malicious.
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.
2:19:30EN → 简体中文调查人员只获准六天,protein 一词只出现过一次主持 Nathan LabenzCognitive Revolution 一周的晨间节目,内容涉及 OpenAI 的智能体群体事件、GPT-6 Astra 的循环 Transformer,以及暂停之争是否已经结束。
2:06:41EN → 简体中文把 J-space 清零,模型的隐藏目标在第一个 Token 上就亮了主持 Nathan Labenz《The Cognitive Revolution》的每周精选:Anthropic 的全局工作空间(global workspace)论文、AI 超级预测者、开放的视频世界模型,以及一款把推理当作数据搬运问题来解的芯片。
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.
30:34ENCerebras Waited Ten Years for a Market That Finally Needed Speed主持 Sarah Guo, Elad GilFounder and CEO Andrew Feldman walks No Priors through wafer-scale chips, a $20 billion OpenAI deal, and why a dinner-plate processor only mattered once AI became an everyday tool.
1:26:16ENMilliseconds to Match: How Criteo Bids on a Billion Anonymous Profiles主持 Nathan LabenzCriteo’s CTO and AI lab head walk through the millisecond auction, the DeepKNN-to-foundation-model stack, and why a live product catalog still beats a frozen LLM.
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.
1:57:29EN → 简体中文前沿视觉模型得分 12.5%:Joseph Nelson 谈计算机视觉的真实水位线主持 Nathan LabenzRoboflow 的首席执行官向 Nathan Labenz 解释,视觉为何仍落后于语言,如何把 SAM 3 蒸馏成温布尔登可用的边缘模型,以及美国为何几乎从未在开源视觉上领先。
44:19ENThe $500-per-Kilogram Line: Why Starcloud Thinks Data Centers Belong in SpaceStarcloud founder Philip Johnston walks through the physics, launch economics, and business model of putting AI compute in orbit.
4:25:13EN → 简体中文架构几乎没变,变的是数据、成本和开放权重主持 Lex FridmanNathan Lambert 和 Sebastian Raschka 带 Lex Fridman 回顾了这一年:训练方法和数据集的变化,远大于模型设计的变化。展望 2026 年,争论的焦点似乎是推理成本、智能体,以及真正开放的模型该归谁。
1:02:19ENA Top Video Model's Half-Life Is 30 Days. fal Runs 600 AnywayThe fal founders tell Sequoia why generative video is a compute-bound problem that language-model shops keep underestimating, and why they will not let any customer marry a single model.
41:32ENMoore's Law Is Slowing. Nvidia's CTO Still Wants 10× a YearMichael Kagan on why Nvidia's $7 billion Mellanox deal, not just more transistors, is how you turn a data center into one computer.
1:22:07ENEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%主持 Nathan LabenzEmily Sands, Stripe’s head of data and AI, explains how a domain-specific payments model, shared embeddings, and mixed rules turn a $1.4 trillion network into compounding advantage.
1:32:47ENA transformer co-author wants a $160 million model no company owns主持 Nathan LabenzIllia Polosukhin tells Nathan Labenz how NEAR turned a payments headache into a stack for private inference, shared-revenue training, and AI agents that still answer to a court.
59:06ENInference Costs Fell 100x. Tri Dao Says There's Another 10x — and NVIDIA's 90% Won't Hold主持 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.
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.
38:10ENPlus Five for Format, Plus Ten if It Compiles主持 Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.
2:12:00ENGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokens主持 Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
45:06ENMike Schroepfer: AI’s Bottleneck Is Electricity, and One Tanker of Fusion Fuel Could Power the U.S. for a Year主持 Jacob EffronMeta’s former CTO tells Redpoint why hyperscaler demand is the climate story, why Llama was an access play, and why tomorrow’s CTO still lives or dies on a priority queue.
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