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242 件
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スライド 3 枚51:11EN
Matthew Berman· 2024年3月22日Inside Groq’s LPU: Compilers, Chip Networks and Fast InferenceMatthew Berman, Andrew Ling, Igor Arsovski · Forward FutureGroq engineers Andrew Ling and Igor Arsovski explain their hardware and software design in a sponsored 2024 interview.インフラとデプロイInferenceAI cost & performance
38:33EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年3月21日ポッドキャスト · 2 人Speed 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.インフラとデプロイInferenceAI cost & performance+3
42:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年3月7日ポッドキャスト · 2 人Meta’s $197 Billion Day Was a Partial Answer to AI’s $200 Billion Questionホスト Sarah Guo, Elad GilSarah Guo and Elad Gil walk through Gemini, Mistral, NVIDIA’s upgrade cycle, Klarna’s support bot, and why the real AI spend still sits with the hyperscalers.企業と戦略AI Strategyエンタープライズ AI+3
15:58EN
Matthew Berman· 2024年3月2日Elon Musk on Self-Driving, AI Compute and Electricity ConstraintsElon Musk, Matthew Berman · TeslaA 2024 Bosch interview and Matthew Berman’s commentary explore autonomous vehicles, robots and the infrastructure needed for AI.Autonomous DrivingAGI TimelinesRobotics+2
39:03EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年2月29日ポッドキャスト · 2 人MI300 Leads Inference by 2x: AMD's CTO on Winning Chip Competition Without a Walled Gardenホスト 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.インフラとデプロイInferenceAI cost & performance+3
スライド 8 枚13:19EN
Matthew Berman· 2024年1月18日Running a Local Language Model as an Offline BackupMatthew Berman · Forward FutureMatthew Berman demonstrates a 2024 local-model setup, from selecting a quantized model and storing it on an external drive to planning backup power.Local inferenceFoundation Modelsインフラとデプロイ+1
3:37EN
Matthew Berman· 2024年1月18日Mark Zuckerberg Announces Llama 3 Training and Meta’s AI PlansMatthew Berman, Mark Zuckerberg · Forward FutureIn January 2024, Matthew Berman reacts to Zuckerberg’s remarks on general intelligence, compute and AI devices.Open-weight modelsAI Historyインフラとデプロイ+1
39:36EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年12月14日ポッドキャスト · 2 人The Most Underestimated AI Buyer Is the Shop on the Cornerホスト Sarah Guo, Elad GilFormer Square CEO Alyssa Henry tells No Priors that generative AI is unlocking work small merchants always needed and never paid anyone to do.企業と戦略Product StrategyAI Adoption+3
55:34EN
Jane Street· 2023年11月29日Andrew Hunter on Performance Engineering for Low-Latency SystemsYaron Minsky, Andrew Hunter · Jane StreetWhy small optimizations pay at hyperscale, why trading latency is harder to measure, and how profiling, tracing, language design and hardware fit together.Software engineeringオブザーバビリティと信頼性Programming Languages+2
スライド 4 枚17:43EN
Matthew Berman· 2023年11月6日OpenAI DevDay 2023: GPT-4 Turbo, Custom GPTs and Developer ToolsMatthew Berman, Sam Altman, Satya Nadella · Forward FutureMatthew Berman reviews OpenAI’s 2023 announcements, from longer context and cheaper API calls to custom GPTs and their implications for developers.OpenAIインフラとデプロイエージェント工学+2
1:19:52EN
Cognitive Revolution "How AI Changes Everything"· 2023年10月18日ポッドキャスト1,000 H100s Take 115 Days; 22,000 Take Fiveホスト Nathan LabenzNathan Labenz and Trey Kollmer walk through cluster math that turns GPT-4-scale training into a weekly experiment, then four papers on search-augmented models, recursive code improvers, spatial maps inside Llama 2, and what networks do when they have no idea.モデルと学習AI researchModel Interpretability+3
スライド 4 枚7:02EN
Matthew Berman· 2023年10月18日Connecting AutoGen to a RunPod-Hosted Model with Text Generation WebUIMatthew Berman · Forward FutureIn a 2023 walkthrough, Matthew Berman connects AutoGen to Dolphin 2.1 Mistral 7B through an OpenAI-compatible API and tests a small coding task.エージェント工学Open-weight modelsMulti-agent systems+2
スライド 3 枚4:42EN
Matthew Berman· 2023年10月17日Connecting AutoGen to a Local Model With LM StudioMatthew Berman · Forward FutureMatthew Berman demonstrates an early local-model setup and explains where agent behavior still needs work.エージェントOpen-weight modelsインフラとデプロイ
30:03EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年9月7日ポッドキャスト · 2 人A Dinner-Plate Chip Cut Training From 60 Days to 3.5ホスト 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.インフラとデプロイInferenceAI cost & performance+3
スライド 4 枚4:35EN
Matthew Berman· 2023年9月5日Running WizardCoder on a Cloud GPU: A 2023 RunPod WalkthroughMatthew Berman · Forward FutureMatthew Berman demonstrates downloading a quantized 13B coding model into a hosted text-generation interface, then explains how to stop the pod and avoid ongoing charges.モデルと学習インフラとデプロイコーディングエージェント
38:13EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年8月31日ポッドキャスト · 2 人ChatGPT Runs on His Framework. He Still Doesn't Want You Training a Modelホスト Sarah Guo, Elad GilVercel founder Guillermo Rauch tells No Priors why frontend is the last mile of AI, why first-generation AI frameworks will be rewritten, and why bots are already stealing your tokens.インフラとデプロイ開発ワークフローとテストオブザーバビリティと信頼性+3
1:01:26EN
Cognitive Revolution "How AI Changes Everything"· 2023年7月25日ポッドキャストAlexNet Used Two GPUs. GPT-4 Took Twenty Thousand. The Shortage Will Be Back.ホスト Nathan LabenzRun:ai CTO Ronen Dar on filling idle accelerators, why H100s can be cheaper per experiment, and why Llama 2 undercuts compute governance.インフラとデプロイAI cost & performanceDistributed training & inference+3
3:08:46EN
Lex Fridman· 2023年6月29日ポッドキャスト · 2 人Who 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.オープンソースとコミュニティインフラとデプロイOpen-source ecosystems+3
スライド 3 枚8:17EN
Matthew Berman· 2023年6月22日Using OpenLLM to Serve Open Models Through an APIMatthew Berman · Forward FutureMatthew Berman demonstrates a local model server, its interactive API and a Python client in a 2023 walkthrough.Open-weight modelsInferenceインフラとデプロイ+1
スライド 5 枚9:46EN
Matthew Berman· 2023年6月19日Installing Text Generation WebUI on Windows: A 2023 Local-Model WalkthroughMatthew Berman · Forward FutureMatthew Berman installs an early version of Text Generation WebUI, resolves setup errors and explains model downloads and interface settings. The steps and packages are historical; consult the project’s current documentation for a modern installation.Local inferenceOpen-weight modelsインフラとデプロイ+1
3:34:03EN
Lex Fridman· 2023年6月2日ポッドキャスト · 2 人Python 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.インフラとデプロイAI platform engineeringSoftware engineering+3
スライド 6 枚11:49EN
Matthew Berman· 2023年6月1日Testing Wizard-Vicuna 30B: Responses, Reasoning, and CodeMatthew Berman · Forward FutureMatthew Berman examines a model marketed as uncensored, demonstrates a 2023 cloud setup, and runs an informal collection of tests.モデルと学習評価(Evals)AI Safety+1
スライド 2 枚7:51EN
Matthew Berman· 2023年5月30日Running Guanaco 65B on a Cloud GPU with Runpod and Text Generation Web UIMatthew Berman · Forward FutureMatthew Berman demonstrates a 2023 workflow for renting a GPU, loading a quantized model and generating text. Model requirements, pricing and interfaces depend on the chosen version.Local inferenceインフラとデプロイOpen-weight models+2
1:20:49EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月25日ポッドキャストA Wafer-Scale Chip So AI Stops Waiting on Amdahlホスト Nathan LabenzAndrew Feldman, CEO of Cerebras, tells Nathan Labenz why one 2.5-trillion-transistor die, sparse linear algebra, and a 16-machine supercomputer are a better shape than a GPU cluster.インフラとデプロイInferenceAI cost & performance+3
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