vllm
https://github.com/vllm-project/vllm
Python
A high-throughput and memory-efficient inference and serving engine for LLMs
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- Issues
- [RFC]: Extension points for backends whose physical layout isn't a permutation of the logical layout (weights, quant scales, KV-cache, MLA, EP)
- Revert "[MoE Refactor] Standardize Humming MoE experts + utilities" (#43373)
- [XPU] fix collecting oneccl version info
- Bump astral-sh/setup-uv from 7.6.0 to 9.0.0
- Enable FlashInfer + FP8 KV cache for text-only requests in Gemma 4 multimodal models
- [Bug] OOM during profile_run with GLM-5.2 PD disaggregation + FlashInfer CUTLASS MoE on H100
- [Bug] v0.24.0: DeepGEMM "Unknown recipe" assertion in FP8 kernel warmup on Blackwell (sm_120) — regression vs 0.23.0
- [Bug]: cached_tokens always equals prompt_tokens in disaggregated prefill (P/D) on the decode node
- Allow GLM-4.7 required tool parsing when strict mode is disabled
- [RFC]: Streaming Derender for Disaggregated Serving
- Docs
- Python not yet supported