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]: The arguments invoked by the tool in the GLM-5 streaming output cannot be parsed into the JSON format.
- [Bug]: Why does setting `--pipeline-parallel-size > 1` result in an OOM error, but `--tensor-parallel-size> 1` does not?
- [Bug]: SM120 / RTX 5090 source build still registers unsupported FlashMLA / FA targets and uses non-SM120 Marlin defaults.
- [Core] Skip np.repeat in _prepare_inputs when all requests are decodes
- [Bug]: Gibberish output and collapsing generation throughput with Qwen3.5-35B-A3B-FP8 and speculative decoding enabled
- [Bug]: Qwen1 use_logn_attn may be unsupported in vLLM
- [WIP][Bugfix] Fix CUDA illegal memory access for GPTQ Marlin MoE with CUDA graphs
- [kv_offload] Fix bare Exception types and add FilterReusedOffloadingManager tests
- [Bug]: Tesla T4 GPU - triton.runtime.errors.OutOfResources: out of resource: shared memory, Required: 81920, Hardware limit: 65536. Reducing block sizes or `num_stages`
- [Bug]: Misleading error message for FP8 quantization: refers to CUDA version instead of GPU compute capability
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