unified-cache-management by ModelEngine-Group

Native Windows vLLM 0.26.0: CPython 3.13, CUDA 12.8, SM 7.5/8.6/8.9/12.0 for RTX 20/30/40/50, OpenAI-compatible serving, Triton/FlashAttention, 10 KV-cache formats, Multi-TurboQuant, and experimental CPU/RAM/NVMe prompt-KV offload. No WSL or Docker. Maintained by aivrar on GitHub, where it has earned 36 stars from the community.

Why is LLM inference slow — and how do you make it fast? A hands-on, first-principles course: roofline → KV cache → quantization → parallelism → vLLM/SGLang, with GPU labs on open models. Maintained by jiahongsigma on GitHub, where it has earned 20 stars from the community.

A GPU cluster manager for high-performance AI model serving (vLLM, SGLang) and on-demand SSH-accessible GPU instances. Maintained by gpustack on GitHub, where it has earned 5,436 stars from the community.