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
- [Build] Fix CUDA arch coverage checks and scoped kernel feature flags
- [Feature]:[New Model] Gemma4UnifiedForConditionalGeneration (google/gemma-4-12B-it)
- [Feature]: Support combining multiple speculative decoding methods (e.g. MTP + ngram)
- [Performance]: Audit RoPE + KV-cache fusion coverage for GQA, MLA, and SparseMLA on CUDA
- [XPU] Unify XPU RMSNorm kernels with vllm_c and drop redundant XPU-specific implementation
- [Bug]: Why does kv-cache-dtype=fp8 OOM more easily than bf16/fp16 on long-context GLM-5.1-AWQ runs?
- [ROCm] Pass vLLM gfx942 FP8 FNUZ bound to DeepEP builds
- [ROCm][CI] Apply xgrammar bitmasks in vLLM on ROCm
- [Bug]: AssertionError: Overwriting existing tensor attribute: weight_loader when serving FP8 model on 2x RTX 5090 (Blackwell)
- [CPU][Bug] Dynamic Speculative Decoding crashes on CPU backend
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