Dao-AILab / quackLinks
A Quirky Assortment of CuTe Kernels
☆374Updated this week
Alternatives and similar repositories for quack
Users that are interested in quack are comparing it to the libraries listed below
Sorting:
- Applied AI experiments and examples for PyTorch☆289Updated 2 months ago
- ☆227Updated this week
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆193Updated this week
- Cataloging released Triton kernels.☆246Updated 6 months ago
- Fast low-bit matmul kernels in Triton☆338Updated this week
- kernels, of the mega variety☆461Updated 2 months ago
- ☆227Updated last year
- A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.☆200Updated this week
- Fastest kernels written from scratch☆308Updated 3 months ago
- A curated collection of resources, tutorials, and best practices for learning and mastering NVIDIA CUTLASS☆203Updated 2 months ago
- extensible collectives library in triton☆88Updated 4 months ago
- A collection of memory efficient attention operators implemented in the Triton language.☆273Updated last year
- ☆85Updated 8 months ago
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆260Updated 2 weeks ago
- PyTorch bindings for CUTLASS grouped GEMM.☆106Updated 2 months ago
- Collection of kernels written in Triton language☆139Updated 3 months ago
- Perplexity GPU Kernels☆413Updated 2 weeks ago
- ☆101Updated 7 months ago
- Triton-based implementation of Sparse Mixture of Experts.☆227Updated 8 months ago
- Zero Bubble Pipeline Parallelism☆411Updated 2 months ago
- ring-attention experiments☆145Updated 9 months ago
- This repository contains the experimental PyTorch native float8 training UX☆224Updated last year
- Fast Hadamard transform in CUDA, with a PyTorch interface☆210Updated last year
- Distributed Compiler based on Triton for Parallel Systems☆930Updated this week
- BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.☆653Updated 3 weeks ago
- KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems☆493Updated last week
- PyTorch bindings for CUTLASS grouped GEMM.☆134Updated 2 weeks ago
- 🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.☆207Updated last week
- Dynamic Memory Management for Serving LLMs without PagedAttention☆405Updated 2 months ago
- ☆107Updated 11 months ago