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
- [Frontend] Keep output_text logprobs on streamed Responses items
- [Feat][Mamba2] Enable internal prefill checkpoints
- [Bugfix][Frontend] Allow selecting Uvicorn HTTP implementation
- [Metrics] Add `vllm:kv_cache_token_usage_perc`
- [Model] Enable sequence-parallel RMSNorm for Qwen3 MoE
- [Refactor] Unify BlockStored event construction in cache_partial_block
- [Performance]: Canonical KV offload layout picks the DMA load path by page size, but copies per fragment
- [Perf][KV Offload] Select the canonical load path by fragment size
- [Feature]: [CPU][GLM5Next] Add native sparse MLA / KeyPool indexer support for GLM-5.3-Flash
- Fix full KV event reporting for sparse cache hits
- Docs
- Python not yet supported