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]: DiffusionGemma fails to start with RuntimeError in _compiled_sample_step during torch.compile (matmul shape mismatch)
- Gemma-4-26B-A4B-NVFP4 + MTP4 is slower on v0.23.0 than v0.21.0 on RTX PRO 6000 Blackwell
- [Spec Decode] Adaptive K: Per-position EMA goodput cost model
- [Spec Decode] Add D-cut: Adaptive Verification Depth Pruning for Batched Speculative Decoding
- fix(harmony-renderer): cap system identity instructions at 4096 chars
- [Bug]: triton_w4a16_gemm assert qzeros.shape == (K // group_size, N // 8) fails on GPTQ models
- [BugFix][Frontend] Add content type validation for Harmony path (GPT-OSS models)
- [RoCm][Build/CI][The Rock] Fix test_gemma4router.py for The Rock due to unstable torch.topk
- [Build] Fix CUDA arch coverage checks and scoped kernel feature flags
- [Feature]: Support combining multiple speculative decoding methods (e.g. MTP + ngram)
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