LLM training in simple, raw C/CUDA

An Open-source book with Modern CUDA learning Notes for Beginners - BF16/FP8/FP4, HGEMM, CuTe, Flash-Attention, etc.

DeepGEMM: clean and efficient BLAS kernel library on GPU

FlashInfer: Kernel Library for LLM Serving

[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.

Tile primitives for speedy kernels

An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).

High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.

Hand-Rolled GPU communications library