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] Gate the FA4 masked-MHA sparse prefill on prefill-query quantization, not KV cache dtype
- [Rust Frontend] Add `--always-constrain-output`
- [Performance]: Triton-based attention runs without tensor cores on Turing (SM75) since Triton 3.3, so T4 prefill is up to 15x slower than FlashInfer
- [Bug]: `top_k_per_row_decode` raises an illegal memory access for runtime topK > 8192 instead of a clean error
- [ROCm][Perf][GLM-5] Fused decode with the MonoKernel in vLLM
- [ROCm][Perf] Qwen3.8-2.4T specialized decode on MI355X (--monokernel)
- [Perf][Attention] Skip nonempty output rows during sparse MLA cleanup
- [Bugfix][Tool Parser] Recover when a stray <tool_call> opens a phantom call in the glm47 parser
- [Kernel] Add opt-in BF16 shared-gate and CUTLASS MoE decode fusions
- [Performance][NIXL] Optimize nixl descriptors for block-outermost kv cache layout
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