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
- [Bug]: lmcache_connector.start_load_kv asserts on degraded LMCache instead of honoring "recompute" fallback
- [RFC]: Model customized KVCache Planning
- [Bugfix][Frontend] Fix http_requests_total metric recording some 4xx errors as 5xx
- [ROCm][Perf] Add Fused Shared Expert (FSE) support for GLM-4.5/6/7
- [ROCm] Add tuned MoE configs for Radeon 8060S (gfx1151)
- [MoE Refactor] Add MoEKernelOracle ABC base class and migrate Unquantized oracle
- [Bug]: vLLM hangs when sending a request with "response_format" key for early unsuppoted models like Minimax-M2.5
- [Perf] Add tuned Triton fused MoE configs for NVIDIA H20 (fp8_w8a8)
- [Bugfix][Structured Output][Spec Decode] Constrain bitmask and trim grammar advance at the reasoning boundary
- [Feature]: Could you help with implement the KV cache pruning feature on vLLM?
- Docs
- Python not yet supported