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Until now, adding web search to open-source models meant hand-wiring orchestration, managing separate keys, and paying latency taxes on every round trip. Today, we're solving that. We're excited to introduce Baseten Hosted Tools and Baseten Grounded Inference to bring real-time web search server-side to open models running on Baseten...

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Baseten

19,565 subscribers

29,259 просмотров • 6 дней назад •via X (Twitter)

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Комментарии: 11

Фото профиля Andrey Styskin
Andrey Styskin6 дней назад

Baseten x Keenable ❤️

Фото профиля Baseten
Baseten6 дней назад

🙌

Фото профиля Ishan Goswami
Ishan Goswami6 дней назад

Exa 🤝 Baseten

Фото профиля kes
kes6 дней назад

Great working with you guys on this!

Фото профиля Baseten
Baseten6 дней назад

💚

Фото профиля Exa Developers
Exa Developers6 дней назад

💚

Фото профиля Teo Gonzalez
Teo Gonzalez6 дней назад

Love to see @baseten 🤝 @ExaAILabs

Фото профиля Baseten
Baseten6 дней назад

@ExaAILabs 💚

Фото профиля Manish Tyagi
Manish Tyagi6 дней назад

Excited to see this unfold!

Фото профиля Turk 🇺🇸
Turk 🇺🇸6 дней назад

@saranormous Seems biased. Mentions Dreamforce happening in SF and says nothing about Grok galaxy which has more overall attendees than any baseball game happening this week in SF

Фото профиля RemoteBrowser
RemoteBrowser6 дней назад

The latency tax framing is the real pain point. The round trips add up fast when the model decides to search mid-generation. Does Hosted Tools keep the search call inside the same inference loop, or is it still a separate hop?

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🚀 Our "technical" marketer might not be looped in, but today is our biggest launch day yet. We're introducing two new products to serve the inference lifecycle: Model APIs and Training. Model APIs are frontier models running on the Baseten Inference Stack, purpose-built for production. Baseten Training (Beta) provides infra and tooling without limitations for AI models destined for production. Huge shoutout to the many partners and customers we've worked with as we built these two new products—more details below.
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🚀 Our "technical" marketer might not be looped in, but today is our biggest launch day yet. We're introducing two new products to serve the inference lifecycle: Model APIs and Training. Model APIs are frontier models running on the Baseten Inference Stack, purpose-built for production. Baseten Training (Beta) provides infra and tooling without limitations for AI models destined for production. Huge shoutout to the many partners and customers we've worked with as we built these two new products—more details below.

Baseten

35,350 просмотров • 1 год назад

DeepSeek-R1 is blowing up right now, but we're not surprised. And not just because we’ve been working closely with DeepSeek to bring these models to production. We've been betting on powerful, open-source models like DeepSeek-R1 from day one. 1/n 🧵
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DeepSeek-R1 is blowing up right now, but we're not surprised. And not just because we’ve been working closely with DeepSeek to bring these models to production. We've been betting on powerful, open-source models like DeepSeek-R1 from day one. 1/n 🧵

Baseten

59,340 просмотров • 1 год назад

🚀 Introducing Dynamic Intelligence for Replit Agent We're unlocking next-level problem solving with: 🧠 Extended Thinking ⚡️ High Power Model 🌐 Web Search Now the Agent thinks deeper, reasons better, and surfs the web to get you complete solutions. This allows Agent to solve challenging and open ended problems like—app performance optimizations, complex debugging, UI overhauls etc. Rolling out to all users starting today!
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🚀 Introducing Dynamic Intelligence for Replit Agent We're unlocking next-level problem solving with: 🧠 Extended Thinking ⚡️ High Power Model 🌐 Web Search Now the Agent thinks deeper, reasons better, and surfs the web to get you complete solutions. This allows Agent to solve challenging and open ended problems like—app performance optimizations, complex debugging, UI overhauls etc. Rolling out to all users starting today!

Replit ⠕

111,754 просмотров • 1 год назад

Today we are announcing Keenable AI, an AI-native index of the best human knowledge we have, starting with the open web. AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average. We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web. Backed by a $26M Seed from Accel and Conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity. The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September. Seven days of benchmarks, stories, and launches ahead. Let your agents search.
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Today we are announcing Keenable AI, an AI-native index of the best human knowledge we have, starting with the open web. AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average. We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web. Backed by a $26M Seed from Accel and Conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity. The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September. Seven days of benchmarks, stories, and launches ahead. Let your agents search.

Andrey Styskin

551,347 просмотров • 28 дней назад

We're proud to be the fastest inference provider on Artificial Analysis, OpenRouter, and Hugging Face for GLM-5.3-Flash, at 122+ TPS. All served from the US only, starting on day 0, with ZDR by default. Stay tuned for updates as our engineers continue to optimize GLM-5.3-Flash for even higher throughput and lower latency.
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We're proud to be the fastest inference provider on Artificial Analysis, OpenRouter, and Hugging Face for GLM-5.3-Flash, at 122+ TPS. All served from the US only, starting on day 0, with ZDR by default. Stay tuned for updates as our engineers continue to optimize GLM-5.3-Flash for even higher throughput and lower latency.

Baseten

23,114 просмотров • 26 дней назад

We’re officially a YOUnicorn! 🦄 Excited to share that You.com just raised $100M Series C at a $1.5B valuation, led by Cox Enterprises. We’ve been heads down building the search infrastructure for the AI and agent future. Soon there will be more AI agents using the web than humans, but today's search wasn't built for this. Agents need deep, contextual information from both public web and internal private data to make real decisions. Our web search API delivers the most up-to-date, accurate, and fastest search results for LLMs and agents. Real benchmarks show we consistently outperform the competition on accuracy and speed while staying cost-effective. Today, we’re answering over 1 billion queries every month across enterprises like DuckDuckGo, Windsurf, Harvey, and many others. There is no other startup of recent years at this scale. Grateful to our wonderful employees, customers, partners, and investors who believe in our vision. The agentic era is here - let's build it together. Try our LLM search APIs.
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We’re officially a YOUnicorn! 🦄 Excited to share that You.com just raised $100M Series C at a $1.5B valuation, led by Cox Enterprises. We’ve been heads down building the search infrastructure for the AI and agent future. Soon there will be more AI agents using the web than humans, but today's search wasn't built for this. Agents need deep, contextual information from both public web and internal private data to make real decisions. Our web search API delivers the most up-to-date, accurate, and fastest search results for LLMs and agents. Real benchmarks show we consistently outperform the competition on accuracy and speed while staying cost-effective. Today, we’re answering over 1 billion queries every month across enterprises like DuckDuckGo, Windsurf, Harvey, and many others. There is no other startup of recent years at this scale. Grateful to our wonderful employees, customers, partners, and investors who believe in our vision. The agentic era is here - let's build it together. Try our LLM search APIs.

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Firecrawl open-sourced "web-agent," a new framework that allows developers to build AI agents capable of autonomously executing "search-scrape-interact" loops on the live web. By running a single command, developers can scaffold a complete project with streaming UI, API, or library templates. The framework uses a "Plan-Act" mechanism and can spin up parallel sub-agents to process multiple websites simultaneously. It is entirely model-agnostic, allowing you to plug in OpenAI, Anthropic (with recent builds defaulting to Claude Opus 4.7), or your own self-hosted models.
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Firecrawl open-sourced "web-agent," a new framework that allows developers to build AI agents capable of autonomously executing "search-scrape-interact" loops on the live web. By running a single command, developers can scaffold a complete project with streaming UI, API, or library templates. The framework uses a "Plan-Act" mechanism and can spin up parallel sub-agents to process multiple websites simultaneously. It is entirely model-agnostic, allowing you to plug in OpenAI, Anthropic (with recent builds defaulting to Claude Opus 4.7), or your own self-hosted models.

Wes Roth

53,653 просмотров • 5 месяцев назад

Notion's AI Meeting Notes run on Baseten, and Baseten's knowledge base runs on Notion. Our teams have been working together closely to push the frontier on both fronts. For AI Meeting Notes, our voice engineers built the fastest, highest-quality, yet lowest-cost solution on the market for speaker-attributed transcripts. We're proud to partner with the Notion team.
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Notion's AI Meeting Notes run on Baseten, and Baseten's knowledge base runs on Notion. Our teams have been working together closely to push the frontier on both fronts. For AI Meeting Notes, our voice engineers built the fastest, highest-quality, yet lowest-cost solution on the market for speaker-attributed transcripts. We're proud to partner with the Notion team.

Baseten

36,374 просмотров • 18 дней назад

We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.
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We're excited to announce our partnership with Coinbase 🛡️ to bring Tavily to x402, the open protocol for internet-native agentic payments. With x402, agents can discover and use Tavily web search at runtime without an API key. Agents use a Base wallet to pay per-request and get instant results. The next billion agents will discover, pay for, and use online services fully autonomously. We’re live on and we’re just getting started. More in the comments.

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🚀 Exciting News! We're thrilled to announce the launch of Qwen Chat ( ) – your new go-to Web UI for interacting with Qwen models! 🌟 💬 Chat effortlessly with our flagship model Qwen2.5-Plus , explore vision-language capabilities with Qwen2-VL-Max , and dive into reasoning models like QwQ and QVQ, code with coding expert Qwen2.5-Coder-32B-Instruct, etc. ✨ Key Features: * Choose & compare multiple models in one interface * Upload documents & get answers based on them * Preview mode with HTML support * Upload images for visual understanding 🌟 Coming Soon: Web search, image generation, voice mode, and more! 🔗 Ready to experience the future? Start to chat with Qwen today 💬 Feel free to send us your feedback and suggestions!

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Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.
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Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.

Arena.ai

512,401 просмотров • 3 месяцев назад

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.
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🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

18,053 просмотров • 18 дней назад

Monitor and control your AI spend on every provider on Our early users save 40% on average. Every week, the price-intelligence-latency frontier shifts, and we expect this trend to continue. Tradeoffs between latency, reasoning, cost, service tier, open source and closed source models are shifting constantly. Router sends every request to the model that's actually best for the task and helps you control what tokens you buy. We benchmark it against real work: ~40% lower cost for the same outputs. Today we're opening it to everyone. Two lines of code or just change your base URL. No Ramp account needed. Free through 2026, first $26 on us. Get an API key today at
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Monitor and control your AI spend on every provider on Our early users save 40% on average. Every week, the price-intelligence-latency frontier shifts, and we expect this trend to continue. Tradeoffs between latency, reasoning, cost, service tier, open source and closed source models are shifting constantly. Router sends every request to the model that's actually best for the task and helps you control what tokens you buy. We benchmark it against real work: ~40% lower cost for the same outputs. Today we're opening it to everyone. Two lines of code or just change your base URL. No Ramp account needed. Free through 2026, first $26 on us. Get an API key today at

Veeral Patel

1,424,754 просмотров • 1 месяц назад

What happens when there are more agents than eyeballs browsing the internet? How will the economics of the internet be reinvented? Parag Agrawal thinks Shapley values may hold the answer. Parag built Twitter over a decade, eventually becoming CEO and selling the company to Elon. He's spent the last three years building Parallel Web Systems, a search engine built for agents instead of humans. His core argument: 1) human click data is a bug. Agents aren't just a new technology, they're a distinct customer – and the feedback loop that made Google great is the wrong signal for the thing actually doing the work; 2) the ad-funded web assumed scarce human attention. If agents show up instead of eyeballs, the business model underneath the internet has to be rebuilt, or good content stops being published. The conversation covers: — why he shipped a search agent before a search engine, and how that let him grow the index incrementally instead of buying a full web crawl up front — the billion-to-billion matching problem: pull the right 1,000 tokens out of a trillion web pages, and your agent uses under half the tokens — going from a 3-second compute budget to 200 milliseconds — why he doesn't think Parallel is a neo-lab: "our output is a complement to a model" — the Google Cloud deal — on GCP, your grounding options are now Google Search or Parallel Search — reinventing the economics of the internet, Shapley values as a payment rail for publishers, and why the company was originally incorporated as Shapley Inc — why routing 2-10% of inference spend to web data would dwarf every content business outside the walled gardens — the web going from pull to push: "call me if this happens" 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision Hosted with my very unwilling co-host Andrew Reed and Sequoia Capital
55:18

What happens when there are more agents than eyeballs browsing the internet? How will the economics of the internet be reinvented? Parag Agrawal thinks Shapley values may hold the answer. Parag built Twitter over a decade, eventually becoming CEO and selling the company to Elon. He's spent the last three years building Parallel Web Systems, a search engine built for agents instead of humans. His core argument: 1) human click data is a bug. Agents aren't just a new technology, they're a distinct customer – and the feedback loop that made Google great is the wrong signal for the thing actually doing the work; 2) the ad-funded web assumed scarce human attention. If agents show up instead of eyeballs, the business model underneath the internet has to be rebuilt, or good content stops being published. The conversation covers: — why he shipped a search agent before a search engine, and how that let him grow the index incrementally instead of buying a full web crawl up front — the billion-to-billion matching problem: pull the right 1,000 tokens out of a trillion web pages, and your agent uses under half the tokens — going from a 3-second compute budget to 200 milliseconds — why he doesn't think Parallel is a neo-lab: "our output is a complement to a model" — the Google Cloud deal — on GCP, your grounding options are now Google Search or Parallel Search — reinventing the economics of the internet, Shapley values as a payment rail for publishers, and why the company was originally incorporated as Shapley Inc — why routing 2-10% of inference spend to web data would dwarf every content business outside the walled gardens — the web going from pull to push: "call me if this happens" 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision Hosted with my very unwilling co-host Andrew Reed and Sequoia Capital

Sonya Huang 🐥

127,791 просмотров • 27 дней назад

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

236,375 просмотров • 19 дней назад

AI Mode in Search introduces new reasoning and tools under the hood with a custom version of Gemini models. For any question, it makes a plan, breaks it down into related subtopics, and runs multiple Google searches to find the most helpful and reliable info. With this query fan-out, AI Mode finds real-time info from both the web and from Google, like facts from our Knowledge Graph, info about the real-world, and shopping details on billions of products. This approach helps you get a greater breadth and depth of information than a traditional search on Google.
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AI Mode in Search introduces new reasoning and tools under the hood with a custom version of Gemini models. For any question, it makes a plan, breaks it down into related subtopics, and runs multiple Google searches to find the most helpful and reliable info. With this query fan-out, AI Mode finds real-time info from both the web and from Google, like facts from our Knowledge Graph, info about the real-world, and shopping details on billions of products. This approach helps you get a greater breadth and depth of information than a traditional search on Google.

Robby Stein

38,664 просмотров • 1 год назад

Today we're incredibly excited to announce Dreamscale Labs (YC F26) (YC F26)! We're an applied research company putting robot brains in the cloud. Robot AI models are growing larger and on-board compute is getting increasingly inaccessible due to cost. We're taking a contrarian bet that the future is moving inference off the robot and into the cloud, while still meeting real-time deadlines required by robot motion. We're building the real-time inference infrastructure that enables frontier physical AI models to run smoothly on your robot. We optimize for low latency across the full stack: from the model, down to the GPU kernels, and over the network. Our mission is to unlock a future of safe and intelligent robots doing meaningful work, and we think this is only possible by letting you deploy and run robot models in the cloud. Reach out to us at founders@dreamscalelabs.com or visit our website at to learn more. Kevin L Antoine Nguyen Eric R
1:50

Today we're incredibly excited to announce Dreamscale Labs (YC F26) (YC F26)! We're an applied research company putting robot brains in the cloud. Robot AI models are growing larger and on-board compute is getting increasingly inaccessible due to cost. We're taking a contrarian bet that the future is moving inference off the robot and into the cloud, while still meeting real-time deadlines required by robot motion. We're building the real-time inference infrastructure that enables frontier physical AI models to run smoothly on your robot. We optimize for low latency across the full stack: from the model, down to the GPU kernels, and over the network. Our mission is to unlock a future of safe and intelligent robots doing meaningful work, and we think this is only possible by letting you deploy and run robot models in the cloud. Reach out to us at [email protected] or visit our website at to learn more. Kevin L Antoine Nguyen Eric R

Chris Yoo

46,750 просмотров • 5 дней назад

Local Reachy Mini conversations wireless looks like magic! You can bring your friend around the house and get WOW effect from anyone! 🔥 Thanks Andi Marafioti for the blog post on how to set this up: Open-source Realtime API powered by llama.cpp: Parakeet -> Gemma 4 E4B -> Qwen3TTS. Just perfect on M5! Pictures, audio and content remain local and private! Next step adding some tools to search on the web or even better interfacing with my Hermes Agent! 🔥
1:07

Local Reachy Mini conversations wireless looks like magic! You can bring your friend around the house and get WOW effect from anyone! 🔥 Thanks Andi Marafioti for the blog post on how to set this up: Open-source Realtime API powered by llama.cpp: Parakeet -> Gemma 4 E4B -> Qwen3TTS. Just perfect on M5! Pictures, audio and content remain local and private! Next step adding some tools to search on the web or even better interfacing with my Hermes Agent! 🔥

Ivan Fioravanti ᯅ

22,458 просмотров • 3 месяцев назад

DeepSeek-V3 dropped today and the LLM world just got turned upside down. Again. Early indicators are that this model completely transforms the closed and open-source model landscapes. Tl;Dr - OSS is now SOTA/Top3 again. Here are the key details to know: - Open source and licensed for commercial use - Beats Llamas, Qwens, GPT-4o, Sonnet 3.5 - MoE w/ 671B params, 37B active per token - 128K-token context window - Distilled o3-style reasoning Deeper dive in 🧵 This is one of the first models that need the horsepower of H200s GPUs, so we’re getting them ready to go. If you’re interested in running DeepSeek-V3, reach out to us about a dedicated deployment on H200s: h/t zhyncs for putting us on this early, Dhruv Singal for getting it running on H200s, and Philip Kiely for the demo!
2:57

DeepSeek-V3 dropped today and the LLM world just got turned upside down. Again. Early indicators are that this model completely transforms the closed and open-source model landscapes. Tl;Dr - OSS is now SOTA/Top3 again. Here are the key details to know: - Open source and licensed for commercial use - Beats Llamas, Qwens, GPT-4o, Sonnet 3.5 - MoE w/ 671B params, 37B active per token - 128K-token context window - Distilled o3-style reasoning Deeper dive in 🧵 This is one of the first models that need the horsepower of H200s GPUs, so we’re getting them ready to go. If you’re interested in running DeepSeek-V3, reach out to us about a dedicated deployment on H200s: h/t zhyncs for putting us on this early, Dhruv Singal for getting it running on H200s, and Philip Kiely for the demo!

Baseten

69,598 просмотров • 1 год назад