Grateful to @deepseek_ai for releasing DeepInfer, initially built primarily on @vllm_project, under Apache 2.0 and crediting the open-source community! We've worked hard on DeepSeek V4.1 Flash inference efficiency and will keep improving with ideas from DeepInfer! Stay tuned 🙌

Great work from @XunzhuoLiu and the vLLM Semantic Router team on D3. All six models, from 0.6B to 27B, now have merged vLLM recipes! D3 ranked #1 on both Jev Decision Index 0.3.2 boards: 66.3 text and 71.3 vision (Oct. 12 snapshot). Run multimodal decisions on your own infrastructure through /v1/systemone—with control over data residency, latency budgets and governance. vLLM serves decision models too: the full D3 family, Clef-Flash, Clef-Omni and Liquid d1, through the System One API. D3 returns choices, yes/no answers and scores with probabilities over text, JSON, images and videos. Useful for routing, classification and scoring without generating a long response. In the recipe benchmark, D3 27B delivers 1.8× the requests/sec of its checkpoint server on 1× MI325X at 64 concurrent clients, using the same mixed workload. Start with a current vLLM nightly: vllm serve vllm-sr/d3 Recipes for all six sizes: github.com/vllm-project/reci… API and supported models: docs.vllm.ai/en/latest/model…

🚀 vLLM v0.32.0 is out! 814 commits · 363 contributors · 121 first-timers 🧭 New /v1/systemone structured-decision endpoint + generic hf tool/reasoning parser 🗜️ Low-precision KV everywhere: NVFP4 on SM8x/90/12x, FP8 KV default for GLM-5.x, 4-bit KV on ROCm 🛠️ Model Runner V2: GPU n-gram speculator, Elastic EP, PP+CP for MLA, now default on ROCm ⚡ Faster startup & serving: precompiled kernel downloads, autotune preload, +43–85% req/s with multi-API workers 🆕 EmbeddingGemma 2, MiniCPM-V 4.7, Nemotron 3.5 ASR 🔒 3 security fixes + CORS hardening Full notes → github.com/vllm-project/vllm…

🚀 @AMD MI355X just showed competitive performance to NVIDIA B300 on @Kimi_Moonshot K3 serving! 8× MI355X delivers 24.4% more throughput than HGX B300 (7,567 vs 6,083 tok/s/GPU) at 50 tok/s/user on @SemiAnalysis_ AgentX agentic coding benchmark, and 87.4% more tokens per dollar. We cover the vLLM optimizations, AMD kernels, and hardware details that closed the gap, plus patterns for spotting bottlenecks so you can get more out of vLLM on your own GPUs. Thanks @AMD @embeddedLLM @SemiAnalysis_ Full writeup: 🔗 vllm.ai/blog/2026-10-16-kimi…