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
- [Feature]: Support Prefix-LM for ROCM_ATTN and ROCM_UNIFIED_ATTN backends
- [Bugfix][MoE] Size the FlashInfer one-sided dispatch for experts that quantize their own inputs
- [RFC][KV Connector] NIXL support for shared in-flight prefix loads
- fix: handle sampling when all tokens are invalid (allowed_token_ids + bad_words contradiction)
- [ROCm] mxfp4 MoE: TRITON_UNFUSED is never auto-selected, leaving pre-CDNA3 (gfx90a) with no working backend
- [ROCm][Perf] Fuse act_and_mul into the AITER MXFP4 GEMM's activation quant
- [Feature]: [ROCm][AITER] Track relu2 (non-gated) MoE activation enablement (Tier 3, vLLM-side integration)
- [RFC][Core] Add a model metadata provider hook for plugins
- [Bug]: GLM-5.3-Flash with HiSparse has no compatible KV cache layout (LBHNC vs BLHNC)
- [Bug] VLLM_BATCH_INVARIANT=1 is not batch-invariant for a pooling (cross-encoder) model on sm_120 — GEMMs fall to the cuBLAS-workspace branch
- Docs
- Python not yet supported