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
- [Test][Core] Cover Mamba-deferred prefix-cache metrics
- [Online quantization] Support activation quant key override
- [XPU] Enable Kimi K3 KDA kernel tests on XPU
- docs: add Sealos deployment option for CPU install
- [Tests][Parser] Add unit tests for tool-call parser metrics
- [MoE][Humming] Enable group-32 W4A16 MoE via compressed-tensors checkpoints
- [Profiler] Export torch profiler trace off the worker's main thread
- Copy CPU shared-memory tails at their exact length
- Preserve cache isolation across Harmony tool turns
- [BugFix] Clamp Mooncake store token_len to hash coverage under MTP/async scheduling
- Docs
- Python not yet supported