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
- fix: release committed encoder data for resumable sessions
- [Bugfix] Enhance reasoning extraction to handle prefixes and missing …
- [Rocm] Fix LMcache support issues for the kimik3-dspark model
- [Bug]: int8_per_token_head KV with prefix caching DISABLED corrupts the FIRST generated tokens (Gemma-4 hybrid, Triton, idle KV pool)
- [Bugfix] Resolve $ref/$defs in tool schemas and preserve definitions
- [Bug][ROCm] Remove `AITER_MXFP4_BF16` and `AITER_MXFP4_FP8` MOE routing hotfix once AITER is bumped
- [Frontend] Add Holo2 reasoning parser
- [XPU] Work around Battlemage oneCCL IPC handle cache device-loss failures
- [Feature] Enable AITER MXFP4 MoE on gfx942 and optimize tile configurations for MI325X Target Kimi K3 running on MI325X
- Remove attention layer name from unified_kv_cache_update for torch.compile
- Docs
- Python not yet supported