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
- [Feature][Cleanup]: Unify RMSNormGated and FusedRMSNormGated
- [Bug]: meta-llama/Llama-3.2-1B-Instruct Fails With ROCM_ATTN Due To Seg Fault
- [Feature]: Per-Request Timing Headers (`--enable-request-stats-headers`)
- [Bug]: kimi-2.5 reasoning_parser error.
- [RFC]: Speculative Decoding Proposer Interface Unification Proposal
- [Bug]: When using VLLM version 0.16.0, there will be an error when loading the qwen3-14b-awq model, such as:ERROR _wrapper.py:141: Error in wrapped target: CUDA error: the provided PTX was compiled with an unsupported toolchain.
- [Bug]: When using VLLM version 0.16.0, there will be an error when loading the qwen3-14b-awq model, such as: ERROR _wrapper.py:141: Error in wrapped target: CUDA error: the provided PTX was compiled with an unsupported toolchain.
- [Bug]: Generation hangs until RAY_CGRAPH_get_timeout (300s) with Ray compiled DAG executor
- [Bugfix] Fix FlashInfer block size restriction breaking hybrid attention models
- [Feature]: Include kv_transfer_params in Streaming Responses to optimize TTFT in P/D Disaggregation
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