The first YOCO (you only look once) model for agentic use with shared kv cache and efficiency. vLLM is the go to production engine for agentic workload!
🐳 DeepSeek-V4.1-Flash is out, and vLLM serves it from day 0, verified on NVIDIA and AMD GPUs! 🎉
552B MoE backbone, native vision, 1M context. Built for agents: 8B active while it reads your prompt, 16B while it writes. If you already run DeepSeek-V4 on vLLM, most of this stack will feel familiar: the hyper-connections, the sliding-window plus compressed sparse attention, DSpark drafting, MXFP4 experts. vLLM has carried all of it since V4 landed.
Two things are new, and both are worth a look:
✨ Engram: a quarter of the checkpoint is n-gram memory the model looks up instead of computes. 197B parameters of it.
✨ Only four layers write compressed KV now. The rest of the model shares it.
Spin it up 👇
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