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
- [MM][CG] Support ViT full CUDA graph for Qwen2.5-Omni
- [Feature] Add DoSA prefill indexer on SM100
- [Performance]: Startup sizing of FlashInfer workspace to recover KV-cache capacity
- [ROCm][Quantization] Add int8 lm_head quantization (per-channel and per-group-32) for RDNA (gfx11/gfx12)
- [Bugfix][Frontend] Reject unsupported nested Anthropic tool-result content
- [Feature][KV Connector][Mooncake] Decode-to-prefill KV reuse for multi-turn P/D
- [Feature][Scheduler] Add adaptive token budget scheduler example
- [Bug]: API server crashes with IndexError in structured-decisions init when tokenizer files are missing (after engine fully loads)
- [Feature][MoE] Use explicit forward-context layouts for routed-expert capture
- [Bug]: [MRV2][Spec Decode] DSpark + enable_adaptive_verification fails at startup (device-side assert in drafter) when max_num_batched_tokens < max_num_seqs × draft query rows
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- Python not yet supported