flashinfer-ai / flashinfer
FlashInfer: Kernel Library for LLM Serving
See what the GitHub community is most excited about today.
FlashInfer: Kernel Library for LLM Serving
Instant neural graphics primitives: lightning fast NeRF and more
Causal depthwise conv1d in CUDA, with a PyTorch interface
CUDA Kernel Benchmarking Library
NCCL Tests
A quantization algorithm for LLM
RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
LLM training in simple, raw C/CUDA
CUDA Library Samples
[ICLR2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
cuVS - a library for vector search and clustering on the GPU
[ARCHIVED] Cooperative primitives for CUDA C++. See https://github.com/NVIDIA/cccl
This is a series of GPU optimization topics. Here we will introduce how to optimize the CUDA kernel in detail. I will introduce several basic kernel optimizations, including: elementwise, reduce, sgemv, sgemm, etc. The performance of these kernels is basically at or near the theoretical limit.