vLLM

@vllm_project • 42,942 subscribers

A high-throughput and memory-efficient inference and serving engine for LLMs. Join https://t.co/lxJ0SfX5pJ to discuss together with the community!

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🎉Announcing Gemma4 on vLLM model launch blog at Explore our detailed blogpost covering Gemma 4's capabilities, first-ever day-0 support across diverse hardware platforms, and ready-to-go deployment recipes! #Gemma4 #vLLM

🎉Announcing Gemma4 on vLLM model launch blog at Explore our detailed blogpost covering Gemma 4's capabilities, first-ever day-0 support across diverse hardware platforms, and ready-to-go deployment recipes! #Gemma4 #vLLM

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🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to Z.ai on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale implementation, automated research, and performance optimization ✨ One task can carry a full dev workflow, from requirements to a deployable product across platforms ✨ Client-side and mobile engineering, including an on-device debugging loop Try it out running it on vLLM today: 🔗

🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to Z.ai on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale implementation, automated research, and performance optimization ✨ One task can carry a full dev workflow, from requirements to a deployable product across platforms ✨ Client-side and mobile engineering, including an on-device debugging loop Try it out running it on vLLM today: 🔗

35,720 görüntüleme • 15 gün önce

🎉 Congrats to MiniMax (official) on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image + video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at MiniMax (official), NVIDIA AI, AI at AMD, and Inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗

🎉 Congrats to MiniMax (official) on releasing MiniMax M3! Frontier coding and agentic capabilities, native image and video input, computer use, and a 1M-token context window, all in a single open model. At the heart of M3 is MSA, a new sparse attention architecture: instead of attending densely over the full KV cache, each query scores 128-token KV blocks and runs attention only over the top blocks. That is what makes 1M-token context practical to serve. M3 runs in vLLM with day-0 support, verified on NVIDIA and AMD hardware: ✨ MSA sparse attention with dedicated prefill and decode kernels ✨ 1M-token context serving with prefix caching and chunked prefill ✨ BF16 and MXFP8 checkpoints, with MoE backends for both Hopper and Blackwell ✨ Native multimodal input (image + video) ✨ Tool calling, reasoning parsing, and thinking-mode control for agent workloads Day-0 support like this is a true team effort. Grateful to the teams at MiniMax (official), NVIDIA AI, AI at AMD, and Inferact, and to the vLLM community for making it happen. 🙏 Deep dive into the implementation, kernel work, and deployment recipes: 🔗

39,959 görüntüleme • 19 gün önce

🎉 Meet vLLM-Omni v0.22.0, a major upgrade for omnimodal world models and production-grade multimodal serving. 🌍 Day-0 NVIDIA AI Cosmos 3 world models: text, image, audio, video, and action, in and out. 🤖 Robot serving: DreamZero + OpenPI realtime API. 🎙️ Production TTS: Qwen3-TTS, Qwen3-Omni, VoxCPM2 and more. 🎨 Faster image/video/diffusion: Wan 2.2, HunyuanVideo 1.5, LTX-2.3. ⚡ Broader quantization (FP8/INT8, MXFP4/MXFP8, W4A16, ModelOpt) and hardware coverage. 339 commits, 124 contributors, 52 of them new. Thank you all. 🙌 🔗

🎉 Meet vLLM-Omni v0.22.0, a major upgrade for omnimodal world models and production-grade multimodal serving. 🌍 Day-0 NVIDIA AI Cosmos 3 world models: text, image, audio, video, and action, in and out. 🤖 Robot serving: DreamZero + OpenPI realtime API. 🎙️ Production TTS: Qwen3-TTS, Qwen3-Omni, VoxCPM2 and more. 🎨 Faster image/video/diffusion: Wan 2.2, HunyuanVideo 1.5, LTX-2.3. ⚡ Broader quantization (FP8/INT8, MXFP4/MXFP8, W4A16, ModelOpt) and hardware coverage. 339 commits, 124 contributors, 52 of them new. Thank you all. 🙌 🔗

41,700 görüntüleme • 23 gün önce

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