< cd ../gallery
[AI & ML]
Efficient-LLM-Inference-Serving-Systems by jiahongsigma
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.
# install
pip install efficient-llm-inference-serving-systemsmore vLLM repos
AI & ML
Unified KV-cache compression for LLM inference: 12 Python-native methods, Debian-tested isolated add-ons, Godzilla KVarN/TriAttention, exact Godzilla/Gigatoken profiles, CUDA weight sharing, and multi-GPU planning. Maintained by aivrar on GitHub, where it has earned 25 stars from the community.
2mo ago
AI & ML
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.
2mo ago
AI & ML
First open-source implementation of Google TurboQuant (ICLR 2026) -- near-optimal KV cache compression for LLM inference. 5x compression with near-zero quality loss. Maintained by OnlyTerp on GitHub, where it has earned 78 stars from the community.
4mo ago
