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Open-source vs. frontier AI is usually a theoretical debate. DataCamp has to make that choice in production - while building an AI-native tutor for a platform serving more than 19 million learners. My co-founder Peter and I sat down with DataCamp Co-Founder and CEO Jonathan Cornelissen and Chief AI...

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Chubby♨️

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18,825 次观看 • 1 个月前 •via X (Twitter)

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Bending Spoons self-hosts open-weight models for ~99% of AI requests and tokens, using frontier models for only ~1% of the most complex work or to check the open models, staying fully vendor-neutral while keeping token costs extremely low. - Luca Ferrari, CEO and co-founder Bending Spoons, a Milan-based Italian tech company
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Bending Spoons self-hosts open-weight models for ~99% of AI requests and tokens, using frontier models for only ~1% of the most complex work or to check the open models, staying fully vendor-neutral while keeping token costs extremely low. - Luca Ferrari, CEO and co-founder Bending Spoons, a Milan-based Italian tech company

Rohan Paul

24,759 次观看 • 21 天前

Livestream Alert: Open World Models in Production: LTX.io and ComfyUI Founders Live A special live conversation with Yoland Yan ( CEO, ComfyUI ) & comfyanonymous ( Founder, ComfyUI ) and Zeev Farbman ( CEO, LTX ) Join us as we sit down with the teams behind LTX and ComfyUI to talk about building an open world model that integrates natively into the production tools creators use. We'll explore what it took to build LTXa foundational model that understands space, motion, and how the physical world operates—with synchronized video and audio generation, truly open weights, and the flexibility to fine-tune on your own data. We'll explore what it took to ship a frontier video model in the open, how LTX and ComfyUI are building the pipeline for AI-driven production together, and what it means for creators, developers, and the wider open-source ecosystem. What we'll cover: → LTX: the open world model for synchronized video + audio, and beyond → Native integration into ComfyUI workflows and production pipelines → Open world models vs. closed platforms and where things are headed → Fine-tuning LTX on your own data with LTX Trainer → Community Q&A — bring your questions
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Livestream Alert: Open World Models in Production: LTX.io and ComfyUI Founders Live A special live conversation with Yoland Yan ( CEO, ComfyUI ) & comfyanonymous ( Founder, ComfyUI ) and Zeev Farbman ( CEO, LTX ) Join us as we sit down with the teams behind LTX and ComfyUI to talk about building an open world model that integrates natively into the production tools creators use. We'll explore what it took to build LTXa foundational model that understands space, motion, and how the physical world operates—with synchronized video and audio generation, truly open weights, and the flexibility to fine-tune on your own data. We'll explore what it took to ship a frontier video model in the open, how LTX and ComfyUI are building the pipeline for AI-driven production together, and what it means for creators, developers, and the wider open-source ecosystem. What we'll cover: → LTX: the open world model for synchronized video + audio, and beyond → Native integration into ComfyUI workflows and production pipelines → Open world models vs. closed platforms and where things are headed → Fine-tuning LTX on your own data with LTX Trainer → Community Q&A — bring your questions

ComfyUI

13,174 次观看 • 2 个月前

Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️
0:20

Open models are accelerating development and deployment across agentic AI, physical AI, and a growing range of industries. ✨ “We have billions of dollars of supercomputers in operation, so we can develop our open models,” said NVIDIA CEO Jensen Huang in a recent keynote. “That work is starting to attract attention across industries worldwide, as we build frontier AI models in many different domains.” This includes leading open models from NVIDIA, including: ⚙️ NVIDIA Nemotron for agentic AI ⚙️ NVIDIA Cosmos and GR00T for physical AI ⚙️ NVIDIA Alpamayo for autonomous vehicles ⚙️ NVIDIA Clara for biomedical AI Learn more about open source and AI ➡️

NVIDIA AI

19,296 次观看 • 8 个月前

AI video companies are no longer claiming they can simply generate better clips. They are claiming their models understand the world. So Peter and I put that claim to the test. We sat down with Yaron Inger, Co-Founder and CTO of LTX.io , for a direct conversation about LTX-2.5, its new open-weights video and world model. We asked what the model actually understands beyond producing plausible frames. Whether its physics are good enough for robotics. What native multi-shot generation really remembers across cuts. Whether its speed benchmarks are fair. What creators can expect on hardware they can realistically afford. And how LTX plans to build a business when the weights are available to download. This is not a product demo. It is a conversation about whether AI video is becoming genuine infrastructure for filmmaking, simulation, and physical AI. Watch the full interview:
1:25:24

AI video companies are no longer claiming they can simply generate better clips. They are claiming their models understand the world. So Peter and I put that claim to the test. We sat down with Yaron Inger, Co-Founder and CTO of LTX.io , for a direct conversation about LTX-2.5, its new open-weights video and world model. We asked what the model actually understands beyond producing plausible frames. Whether its physics are good enough for robotics. What native multi-shot generation really remembers across cuts. Whether its speed benchmarks are fair. What creators can expect on hardware they can realistically afford. And how LTX plans to build a business when the weights are available to download. This is not a product demo. It is a conversation about whether AI video is becoming genuine infrastructure for filmmaking, simulation, and physical AI. Watch the full interview:

Chubby♨️

22,892 次观看 • 1 个月前

Europe talks constantly about AI sovereignty. But in practice, much of the continent still seems to want American AI with a European postcode. Together with Superintelligence co-founder Peter Thum, I sat down with PandaOS co-founders Philipp Türker and Marco Szeidenleder to examine what technological sovereignty actually means when European companies remain dependent on US models, cloud providers, chips, APIs, and infrastructure. The conversation began with Europe’s recent Anthropic warning shot: what happens when access to a frontier model can suddenly disappear, prices change, or a provider becomes unavailable? Philipp and Marco argue that sovereignty should not mean attempting to own every layer of the AI stack. It means maintaining control over the systems and data that are essential to your business—and preserving the ability to switch providers without rebuilding your entire operation. Have fun! YouTube down below
1:08:05

Europe talks constantly about AI sovereignty. But in practice, much of the continent still seems to want American AI with a European postcode. Together with Superintelligence co-founder Peter Thum, I sat down with PandaOS co-founders Philipp Türker and Marco Szeidenleder to examine what technological sovereignty actually means when European companies remain dependent on US models, cloud providers, chips, APIs, and infrastructure. The conversation began with Europe’s recent Anthropic warning shot: what happens when access to a frontier model can suddenly disappear, prices change, or a provider becomes unavailable? Philipp and Marco argue that sovereignty should not mean attempting to own every layer of the AI stack. It means maintaining control over the systems and data that are essential to your business—and preserving the ability to switch providers without rebuilding your entire operation. Have fun! YouTube down below

Chubby♨️

15,411 次观看 • 1 个月前

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.
1:05

Today we're announcing Base Labs, a dedicated research organization focused on advancing open-source AI. We believe in a healthy, open frontier model ecosystem. To enable this, we are working on: - Blue-sky research on continual learning, the science of RL, and how models learn across their full lifecycle, with every experiment and recipe shared openly. - The BaseHub Data Foundry: the highest-quality open RL environments, training data, and real-world benchmarks, built for anyone to train and benchmark on. - Post-post training: taking open-source models and making them better, safer, and more aligned through continual post-training, built on our research, and deployed with our frontier safety stack so organizations can use open-source models with confidence. - Making models cheaper and more performant through our model performance research. This is a mission-driven research effort, not a commercial product. We believe the health of the open-source AI ecosystem matters and that the best way to advance it is to do science in the open. We’re hiring engineers, researchers, and research fellows to advance this mission.

Base Labs

243,728 次观看 • 1 个月前

Uber CEO Dara Khosrowshahi: "We Blew Through Our AI Budget in a Quarter" In response, Uber is doing three things: -Slowing headcount growth as AI makes engineers more productive -Reserving expensive frontier models for exploration only and shifting production workloads to cheaper open source models -Capping each employee at $1,500/month per AI coding tool
1:06

Uber CEO Dara Khosrowshahi: "We Blew Through Our AI Budget in a Quarter" In response, Uber is doing three things: -Slowing headcount growth as AI makes engineers more productive -Reserving expensive frontier models for exploration only and shifting production workloads to cheaper open source models -Capping each employee at $1,500/month per AI coding tool

Wall Street Rollup

99,031 次观看 • 4 个月前

Sebastian Siemiatkowski, the co-founder and CEO of Klarna: SaaS is Dead. "The cost of creating software is going down to zero. And that means everyone will be able to generate software at any point in time. " But the real threat to SaaS will shows up next when AI makes data migration one-click—no more hostage data models, no more painful switching costs. --- From '20VC with Harry Stebbings' YT channel ( link in comment)
1:17

Sebastian Siemiatkowski, the co-founder and CEO of Klarna: SaaS is Dead. "The cost of creating software is going down to zero. And that means everyone will be able to generate software at any point in time. " But the real threat to SaaS will shows up next when AI makes data migration one-click—no more hostage data models, no more painful switching costs. --- From '20VC with Harry Stebbings' YT channel ( link in comment)

Rohan Paul

93,544 次观看 • 7 个月前

As agentic automation helps developers get more done, the role is evolving toward higher-level decisions about what to build, how systems should work, and where taste and creativity matter most. Databricks Co-founder Patrick Wendell, OpenClaw creator Peter Steinberger 🦞, and Codex engineering lead Tibo discuss what that shift means for enterprises building, governing, and scaling fleets of AI agents in production. Watch the full conversation:
2:06

As agentic automation helps developers get more done, the role is evolving toward higher-level decisions about what to build, how systems should work, and where taste and creativity matter most. Databricks Co-founder Patrick Wendell, OpenClaw creator Peter Steinberger 🦞, and Codex engineering lead Tibo discuss what that shift means for enterprises building, governing, and scaling fleets of AI agents in production. Watch the full conversation:

Databricks

13,419 次观看 • 1 个月前

> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI
57:20

> "In no scenario is it bad for the US to have frontier-class open-source models." > "Every time there’s a step-function improvement in AI, a human bottleneck emerges alongside it." > "Your desire to be liked sometimes takes away from your desire to do the right thing and seek the truth." > "Every job will get refactored. The people who aren’t AI fluent will have a very hard time finding jobs." Jeetu Patel (Jeetu Patel), President & Chief Product Officer at Cisco sat down for an expansive talk about life and the future. 00:00 AI, jobs, and human potential 00:41 AI cybersecurity and Cisco’s security models 06:33 Why Cisco released the models openly 08:08 Running AI models locally 10:12 Why people are negative about AI 12:14 Will humans always be needed? 14:56 Can curiosity and ambition be taught? 17:12 Passion vs. expertise 19:39 The importance of unlearning 23:52 How to find the truth 26:06 How to get people excited about AI 33:35 How open source affects the AI industry 35:07 Can open-source AI make money? 46:44 Are Chinese open-source models good for the US? 49:55 Should the US restrict Chinese AI models? 53:05 Does open source make humanity safer? 54:11 Advice for people worried about AI

Matthew Berman

69,339 次观看 • 2 个月前

NVIDIA CEO, Jensen Huang, warns that America's lead in AI is far from secure. He breaks down the US-China AI competition into what he calls a "five-layer cake." And while the US dominates some layers, Jensen sees critical vulnerabilities in others... 1) Energy: China has twice as much as the US despite a smaller economy. Which "makes no sense" to Jensen. 2) Chips: The US is "generations ahead," but Jensen warns against complacency. "Anybody who thinks China can't manufacture is missing the big idea." 3) Infrastructure: Standing up a data center in the US takes about three years. In China? "They can build a hospital in a weekend." 4) Models: US frontier models are "unquestionably world class," but "China is well ahead, way ahead on open source." 5) Applications: Public sentiment differs sharply. Ask both populations whether AI will do more good than harm, and "in their case 80% would say AI will do more good than harm. In our case, it'd be the other way around." Jensen's warning is clear. Leading in chips and frontier models isn't enough when you're behind on energy, infrastructure speed, open source, and public trust. Winning the AI race requires strength across the entire stack, and right now, the US has work to do.
1:39

NVIDIA CEO, Jensen Huang, warns that America's lead in AI is far from secure. He breaks down the US-China AI competition into what he calls a "five-layer cake." And while the US dominates some layers, Jensen sees critical vulnerabilities in others... 1) Energy: China has twice as much as the US despite a smaller economy. Which "makes no sense" to Jensen. 2) Chips: The US is "generations ahead," but Jensen warns against complacency. "Anybody who thinks China can't manufacture is missing the big idea." 3) Infrastructure: Standing up a data center in the US takes about three years. In China? "They can build a hospital in a weekend." 4) Models: US frontier models are "unquestionably world class," but "China is well ahead, way ahead on open source." 5) Applications: Public sentiment differs sharply. Ask both populations whether AI will do more good than harm, and "in their case 80% would say AI will do more good than harm. In our case, it'd be the other way around." Jensen's warning is clear. Leading in chips and frontier models isn't enough when you're behind on energy, infrastructure speed, open source, and public trust. Winning the AI race requires strength across the entire stack, and right now, the US has work to do.

Big Brain AI

59,540 次观看 • 9 个月前

Open source AI, the future of Python development, journalism in the age of AI, and more... I sat down with Simon Willison to chat about this, what he is working on these days, and where open source AI is going. Simon is the best, I hope you enjoy a glimpse into the future.
54:46

Open source AI, the future of Python development, journalism in the age of AI, and more... I sat down with Simon Willison to chat about this, what he is working on these days, and where open source AI is going. Simon is the best, I hope you enjoy a glimpse into the future.

Logan Kilpatrick

68,668 次观看 • 1 年前

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
0:26

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 次观看 • 2 个月前

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC
57:15

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC

Y Combinator

313,654 次观看 • 1 个月前

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."
0:35

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

14,114 次观看 • 2 个月前

In Signal Dialogues #04, we explore what it takes to build the foundational layer of voice AI from the ground up. Karan Goel, Co-founder and CEO of Cartesia, sits down with Aakrit Vaish to unpack the bet behind one of the most ambitious infrastructure plays in AI. Karan and his co-founders came out of Stanford's AI Lab, where Albert Gu (now Cartesia's Chief Scientist) co-authored Mamba and pioneered State Space Models, the architecture they believe will define the next generation of real-time, interactive AI. Backed by Kleiner Perkins, Index Ventures, Lightspeed and NVIDIA with over $120M raised, Cartesia is building voice models engineered for real-time latency and efficiency at scale. If you're a founder, researcher or builder thinking about voice, infrastructure or the next architectural shift in AI, this one's for you. Timestamps: 00:00 - Introduction 02:46 - What Cartesia Does: Interactive AI Models 03:24 - Founding Story: From Stanford PhDs to Cartesia 04:03 - The Unique Insight: Efficiency and Beyond Transformers 09:07 - Voice Company, Neolab, or Big Lab? 14:57 - State Space Models and Algorithmic Innovation 26:04 - The Four Big Bets in Interactive AI 30:24 - Products, Customers, and Going Full-Stack 41:44 - The BPO Disruption: India's Call Center Future 54:17 - Competition, Moats, and the Long Game Karan Goel Aakrit Vaish Cartesia
1:01:29

In Signal Dialogues #04, we explore what it takes to build the foundational layer of voice AI from the ground up. Karan Goel, Co-founder and CEO of Cartesia, sits down with Aakrit Vaish to unpack the bet behind one of the most ambitious infrastructure plays in AI. Karan and his co-founders came out of Stanford's AI Lab, where Albert Gu (now Cartesia's Chief Scientist) co-authored Mamba and pioneered State Space Models, the architecture they believe will define the next generation of real-time, interactive AI. Backed by Kleiner Perkins, Index Ventures, Lightspeed and NVIDIA with over $120M raised, Cartesia is building voice models engineered for real-time latency and efficiency at scale. If you're a founder, researcher or builder thinking about voice, infrastructure or the next architectural shift in AI, this one's for you. Timestamps: 00:00 - Introduction 02:46 - What Cartesia Does: Interactive AI Models 03:24 - Founding Story: From Stanford PhDs to Cartesia 04:03 - The Unique Insight: Efficiency and Beyond Transformers 09:07 - Voice Company, Neolab, or Big Lab? 14:57 - State Space Models and Algorithmic Innovation 26:04 - The Four Big Bets in Interactive AI 30:24 - Products, Customers, and Going Full-Stack 41:44 - The BPO Disruption: India's Call Center Future 54:17 - Competition, Moats, and the Long Game Karan Goel Aakrit Vaish Cartesia

Activate

12,489 次观看 • 4 个月前

Our CEO and Co-Founder, Anastasios Nikolas Angelopoulos, joined Harry Stebbings on 20VC to share his thoughts on some of the most consequential technical, economic, and security questions shaping AI: - U.S. vs China competition in frontier and open-weight models - The future of the U.S. open-source AI ecosystem - Kimi K3 release and what it signals about China’s model capabilities - Gravity of the recent cyber security incidents - Which neo-labs will breakthrough into durable businesses - The next generation of business moats: proprietary data, network effects, and ownership of the intelligence stack Watch the full podcast below.
0:46

Our CEO and Co-Founder, Anastasios Nikolas Angelopoulos, joined Harry Stebbings on 20VC to share his thoughts on some of the most consequential technical, economic, and security questions shaping AI: - U.S. vs China competition in frontier and open-weight models - The future of the U.S. open-source AI ecosystem - Kimi K3 release and what it signals about China’s model capabilities - Gravity of the recent cyber security incidents - Which neo-labs will breakthrough into durable businesses - The next generation of business moats: proprietary data, network effects, and ownership of the intelligence stack Watch the full podcast below.

Arena.ai

26,319 次观看 • 2 个月前

vLLM runs on half a million GPUs at any given moment. Most people have never heard of it. Simon Mo, co-founder and CEO of Inferact and lead maintainer of vLLM, sits down with a16z’s Matt Bornstein and Elena Burger to discuss what it takes to actually run open models in production, the advantages of open models, Simon’s mission at Inferact, and more. 00:00 Intro 01:46 When open source became critical infrastructure 08:55 Day zero model releases, and the drama behind them 14:59 What Kimi K3 actually buys you 18:56 Why open model licenses are changing 22:24 The pharmaceutical analogy for funding model training 26:16 If GPUs got 99% cheaper 29:08 Why vLLM, OpenRouter, and Ollama all started before ChatGPT 35:42 Building a company on an open source project 39:48 The inventor of RoPE removing RoPE Simon Mo Matt Bornstein Elena
46:17

vLLM runs on half a million GPUs at any given moment. Most people have never heard of it. Simon Mo, co-founder and CEO of Inferact and lead maintainer of vLLM, sits down with a16z’s Matt Bornstein and Elena Burger to discuss what it takes to actually run open models in production, the advantages of open models, Simon’s mission at Inferact, and more. 00:00 Intro 01:46 When open source became critical infrastructure 08:55 Day zero model releases, and the drama behind them 14:59 What Kimi K3 actually buys you 18:56 Why open model licenses are changing 22:24 The pharmaceutical analogy for funding model training 26:16 If GPUs got 99% cheaper 29:08 Why vLLM, OpenRouter, and Ollama all started before ChatGPT 35:42 Building a company on an open source project 39:48 The inventor of RoPE removing RoPE Simon Mo Matt Bornstein Elena

a16z

210,878 次观看 • 2 个月前