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]: /v1/chat/completions/batch accepts stream: true and returns empty output with HTTP 200; tools are silently dropped
- [Bugfix] Reject stream and tools in batch chat completions instead of returning empty output
- [Quantization] Fix online MoE weight preparation
- [Quantization] Add per-token NVFP4 CuTe-DSL MoE backend
- [Attention][MiniMax-M3] Add MSA speculative decode verification
- [Perf] Use Triton moe backend for tensor fp8 quant scheme on Hopper
- fix(entrypoints): reject stream=True and tools in BatchChatCompletionRequest
- fix(utils): advance next_port when candidate_port is in reserved DP range in get_open_port
- [KV Offloading] Back-pressure detection and remediation
- LoRA on a compressed-tensors int4 (W4A16) model: rank-32 all-layer adapters produce weak, non-reproducible outputs; rank-8 partial-coverage adapters work fine (0.17.1–0.24.0, RTX 5090)
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