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51:11ENInside 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.
38:33ENSpeed Is the Thing You Can Buy: Baseten’s Bet on InferenceAnime : 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.
42:14ENMeta’s $197 Billion Day Was a Partial Answer to AI’s $200 Billion QuestionAnime : 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.
15:58ENElon 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.
39:03ENMI300 Leads Inference by 2x: AMD's CTO on Winning Chip Competition Without a Walled GardenAnime : 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.
13:19ENRunning 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.
3:37ENMark 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.
39:36ENThe Most Underestimated AI Buyer Is the Shop on the CornerAnime : 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.
55:34ENAndrew 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.
17:43ENOpenAI 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.
1:19:52EN1,000 H100s Take 115 Days; 22,000 Take FiveAnime : 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.
7:02ENConnecting 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.
4:42ENConnecting 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.
30:03ENA Dinner-Plate Chip Cut Training From 60 Days to 3.5Anime : 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.
4:35ENRunning 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.
38:13ENChatGPT Runs on His Framework. He Still Doesn't Want You Training a ModelAnime : 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.
1:01:26ENAlexNet Used Two GPUs. GPT-4 Took Twenty Thousand. The Shortage Will Be Back.Anime : Nathan LabenzRun:ai CTO Ronen Dar on filling idle accelerators, why H100s can be cheaper per experiment, and why Llama 2 undercuts compute governance.
3:08:46ENWho owns the off switch? George Hotz on tinygrad, open-source AI, and GodInvité : George Hotz · Anime : 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.
8:17ENUsing 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.
9:46ENInstalling 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.
3:34:03ENPython Isn't Finished: Chris Lattner on Mojo and the Hardware MessInvité : Chris Lattner · Anime : 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.
11:49ENTesting 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.
7:51ENRunning 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.
1:20:49ENA Wafer-Scale Chip So AI Stops Waiting on AmdahlAnime : 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.
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