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
- [Bugfix][Frontend] Count reasoning tokens for the Muse Glimmer parser
- [BugFix] V2 model runner: guard reused pinned input buffers under async scheduling
- [Bug]: Kimi-K3 parsers miss channel terminators - tool calls silently dropped, message terminator leaked into streamed content
- [Bug]: 0.28.0 and 0.29.0 consume all host memory at start and freeze (OK with 0.27.1)
- [ROCm/MoRIIO] Route READ-mode remote KV load via async lifecycle (fix scheduler req_id_to_index KeyError)
- [Bugfix] Honor safetensors index tensor assignments
- [Regression 0.27→0.28+]: GlmMoeDsa (GLM-5.3) + decode-context-parallel: crashes on 0.28.0, silently returns random tokens on 0.29.0
- [Examples] Add SynapticChain native HTTP 402 pay-per-token vLLM proxy
- [Benchmark] Add multilingual character throughput metrics to bench serve (#51963)
- [Bugfix] Keep the frontend profiler on one thread, export off it
- Docs
- Python not yet supported