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]: VLLM_CPU_OMP_THREADS_BIND=nobind cannot be used with tp>1 on CPU backends
- [RFC]: Incremental MoE Expert Offloading — GPU Cache + Async Pipeline
- [Usage]: How to do offline inference on one rank in a distributed environment?
- [RFC]: Multi-tier KV offloading via the vLLM offloading connector
- [Feature]: PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference
- [Bug]: Gemma3n concurrent audio requests crash EngineCore — missing dynamic_dims on audio sequence dimension
- Energy Efficiency: 10 Mathematical Techniques for 60-70% AI Energy Reduction (Phi6Simple, FFT-Mix, Phi MoE)
- [CI] Swap to smaller models for MIG slice compatibility
- [Feature]: support affinity settings in helm chart
- [Bug] Step-3.5-Flash MTP Speculative Decoding Has Extremely Low Acceptance Rate (2.4%-4.6%)
- Docs
- Python not yet supported