Introducing Composer 2.5, our most powerful model yet. It's more intelligent, better at sustained work on long-running tasks, and more reliable at following complex instructions. For the next week, we’re doubling the included usage of the model.

We're onto Inferact's second office this year! Yesterday, we finally broke it in with an office warming. It's amazing to see how far we've come. The vLLM ecosystem has been growing at lightning pace, and we've been lucky to scale alongside it: helping teams serve inference faster, cheaper, and at scale. Thank you to everyone who made it out yesterday — customers, partners, friends, and the whole Inferact team. It meant a lot to celebrate this milestone together. We're hiring across all teams. If you want to join one of the fastest-growing AI infra companies and power the next generation of AI, check out our careers page or DM us. Excited for many more office warmings to come!

Muse Spark debuts at #7 in the Code Arena - making @AIatMeta the #3 lab right behind @AnthropicAI’s Claude Sonnet 4.6 and @Zai_org’s GLM-5.1, surpassing Gemini-3.1-Pro and GPT-5.4. Code Arena evaluates agentic coding on real-world tasks - building live websites and apps, ranked by users on real workflows. Huge congrats to @AIatMeta on this impressive milestone!

Today we're announcing Core Automation Our objective: systems that optimize and automate work, starting with research itself.

We're releasing a technical report describing how Composer 2 was trained.

Since xAI was formed just 30 months ago, the small and talented team has made remarkable progress. The future has never looked more exciting!

SpaceX has acquired xAI, forming one of the most ambitious, vertically integrated innovation engines on (and off) Earth → spacex.com/updates#xai-joins…

Today, we're proud to announce @inferact, a startup founded by creators and core maintainers of @vllm_project, the most popular open-source LLM inference engine. Our mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. The Challenge Inference is not solved. It's getting harder. Models grow larger. New architectures proliferate: mixture-of-experts, multimodal, agentic. Every breakthrough demands new infrastructure. Meanwhile, hardware fragments: more accelerators, more programming models, and more combinations to optimize. The capability gap between models and the systems that serve them is widening. Left this way, the most capable models remain bottlenecked and with full scope of their capabilities accessible only to those who can build custom infrastructure. Close the gap, and we unlock new possibilities. And the problem is growing. Inference is shifting from a fraction of compute to the majority: test-time compute, RL training loops, synthetic data. We see a future where serving AI becomes effortless. Today, deploying a frontier model at scale requires a dedicated infrastructure team. Tomorrow, it should be as simple as spinning up a serverless database. The complexity doesn't disappear; it gets absorbed into the infrastructure we're building. Why Us vLLM sits at the intersection of models and hardware: a position that took years to build. When model vendors ship new architectures, they work with us to ensure day-zero support. When hardware vendors develop new silicon, they integrate with vLLM. When teams deploy at scale, they run vLLM, from frontier labs to hyperscalers to startups serving millions of users. Today, vLLM supports 500 model architectures, runs on 200 accelerator types, and powers inference at global scale. This ecosystem, built with 2,000 contributors, is our foundation. We've been stewards of this engine since its first commit. We know it inside out. We deployed it at frontier scale—in research and in production. Open Source vLLM was built in the open. That's not changing. Inferact exists to supercharge vLLM adoption. The optimizations we develop flow back to the community. We plan to push vLLM's performance further, deepen support for emerging model architectures, and expand coverage across frontier hardware. The AI industry needs inference infrastructure that isn't locked behind proprietary walls. Join Us Through the open source community, we are fortunate to work with some of the best people we know. For @inferact, we're hiring engineers and researchers to work at the frontier of inference, where models meet hardware at scale. Come build with us. We're fortunate to be supported by investors who share our vision, including @a16z and @lightspeedvp who led our $150M seed, as well as @sequoia, @AltimeterCap, @Redpoint, @ZhenFund, The House Fund, @strikervp, @LaudeVentures, and @databricks. - @woosuk_k, @simon_mo_, @KaichaoYou, @rogerw0108, @istoica05 and the rest of the founding team

Today, we're proud to announce @inferact, a startup founded by creators and core maintainers of @vllm_project, the most popular open-source LLM inference engine. Our mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. The Challenge Inference is not solved. It's getting harder. Models grow larger. New architectures proliferate: mixture-of-experts, multimodal, agentic. Every breakthrough demands new infrastructure. Meanwhile, hardware fragments: more accelerators, more programming models, and more combinations to optimize. The capability gap between models and the systems that serve them is widening. Left this way, the most capable models remain bottlenecked and with full scope of their capabilities accessible only to those who can build custom infrastructure. Close the gap, and we unlock new possibilities. And the problem is growing. Inference is shifting from a fraction of compute to the majority: test-time compute, RL training loops, synthetic data. We see a future where serving AI becomes effortless. Today, deploying a frontier model at scale requires a dedicated infrastructure team. Tomorrow, it should be as simple as spinning up a serverless database. The complexity doesn't disappear; it gets absorbed into the infrastructure we're building. Why Us vLLM sits at the intersection of models and hardware: a position that took years to build. When model vendors ship new architectures, they work with us to ensure day-zero support. When hardware vendors develop new silicon, they integrate with vLLM. When teams deploy at scale, they run vLLM, from frontier labs to hyperscalers to startups serving millions of users. Today, vLLM supports 500 model architectures, runs on 200 accelerator types, and powers inference at global scale. This ecosystem, built with 2,000 contributors, is our foundation. We've been stewards of this engine since its first commit. We know it inside out. We deployed it at frontier scale—in research and in production. Open Source vLLM was built in the open. That's not changing. Inferact exists to supercharge vLLM adoption. The optimizations we develop flow back to the community. We plan to push vLLM's performance further, deepen support for emerging model architectures, and expand coverage across frontier hardware. The AI industry needs inference infrastructure that isn't locked behind proprietary walls. Join Us Through the open source community, we are fortunate to work with some of the best people we know. For @inferact, we're hiring engineers and researchers to work at the frontier of inference, where models meet hardware at scale. Come build with us. We're fortunate to be supported by investors who share our vision, including @a16z and @lightspeedvp who led our $150M seed, as well as @sequoia, @AltimeterCap, @Redpoint, @ZhenFund, The House Fund, @strikervp, @LaudeVentures, and @databricks. - @woosuk_k, @simon_mo_, @KaichaoYou, @rogerw0108, @istoica05 and the rest of the founding team