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
- [Attention][MiniMax-M3] Add opt-in FlashInfer TRTLLM-GEN sparse decode
- [Benchmark] Add FP8 block-scaled GEMM to fp8_gemm benchmark
- Detect ROCm wheel variant from environment for precompiled wheels.
- [Bugfix] Fix Aria garbage output at TP>1: shared experts double all-reduce
- [Perf] Split-reduction batch-invariant log_softmax for small-batch decode
- [Bug]: GLM-4.1V fails to start at tensor-parallel size 32 — vision tower head-count mismatch
- [Usage]: DSpark much slower than no-spec on single B300 (DeepSeek-V4-Flash) — config check, or not effective on a saturated batch yet?
- [Bugfix][ROCm] Fix ROCM_AITER_FA & ROCM_AITER_UNIFIED_ATTN QK-Norm+RoPE+KVCache fusion for the packed KV-cache [BLOCKS, HEADS, BLOCK_SIZE, 2*HEAD_DIM] layout
- [ROCm][CI] Add More AITER quantization/MoE kernel tests
- [Bugfix] Aria: set reduce_results=False on shared_experts to fix TP>1
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