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
- [Bugfix] Fix int4_per_token_head for SWA models and gemma 4
- [Bug]: Gemma4 MTP speculative decoding crashes at engine init on 0.25.1 — "a and b must have same reduction dim" (regression from 0.21.0)
- [Bug]: sample_tokens RPC timeout with GLM-5.2-FP8 + DSpark speculative decoding, TP=8 across 2 nodes (Blackwell GB200)
- [RFC]: Context-length-aware speculative token scheduling — extending num_speculative_tokens_per_batch_size with a context-length axis
- `NixlPushMode` Roadmap - Reliability Issue Inventory
- [Perf][Kernel] Fused DSA indexer Top-k kernel (LiteTopk)
- [Perf] Reduce Triton recompiles for multimodal attention ranges
- [Bug]: Spurious EngineDeadError traceback logged during graceful shutdown (AsyncLLM.shutdown cancels output_handler after engine teardown)
- [XPU] Fix Dockerfile.xpu: add /opt/venv/lib to LD_LIBRARY_PATH
- fix: Add FP8 type dispatch to per_token_group_quant_8bit for transformers backend
- Docs
- Python not yet supported