gau-nernst / learn-cudaLinks
Learn CUDA with PyTorch
☆35Updated last week
Alternatives and similar repositories for learn-cuda
Users that are interested in learn-cuda are comparing it to the libraries listed below
Sorting:
- ring-attention experiments☆149Updated 10 months ago
- Cataloging released Triton kernels.☆252Updated 7 months ago
- Small scale distributed training of sequential deep learning models, built on Numpy and MPI.☆136Updated last year
- Fast low-bit matmul kernels in Triton☆353Updated last week
- A curated collection of resources, tutorials, and best practices for learning and mastering NVIDIA CUTLASS☆211Updated 3 months ago
- ☆232Updated this week
- ☆88Updated last year
- This repository contains the experimental PyTorch native float8 training UX☆224Updated last year
- ☆163Updated last year
- extensible collectives library in triton☆88Updated 4 months ago
- A bunch of kernels that might make stuff slower 😉☆58Updated this week
- A repository to unravel the language of GPUs, making their kernel conversations easy to understand☆190Updated 2 months ago
- Load compute kernels from the Hub☆244Updated this week
- a minimal cache manager for PagedAttention, on top of llama3.☆118Updated last year
- Collection of kernels written in Triton language☆147Updated 4 months ago
- PTX-Tutorial Written Purely By AIs (Deep Research of Openai and Claude 3.7)☆66Updated 5 months ago
- Explore training for quantized models☆22Updated last month
- Write a fast kernel and run it on Discord. See how you compare against the best!☆52Updated this week
- Applied AI experiments and examples for PyTorch☆291Updated this week
- 🚀 Efficiently (pre)training foundation models with native PyTorch features, including FSDP for training and SDPA implementation of Flash…☆261Updated last month
- Fault tolerance for PyTorch (HSDP, LocalSGD, DiLoCo, Streaming DiLoCo)☆383Updated last week
- A Quirky Assortment of CuTe Kernels☆411Updated this week
- Triton-based implementation of Sparse Mixture of Experts.☆233Updated 8 months ago
- The simplest but fast implementation of matrix multiplication in CUDA.☆38Updated last year
- High-Performance SGEMM on CUDA devices☆97Updated 7 months ago
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆207Updated this week
- Custom kernels in Triton language for accelerating LLMs☆24Updated last year
- A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.☆568Updated 2 weeks ago
- Mixed precision training from scratch with Tensors and CUDA☆24Updated last year
- ☆192Updated 7 months ago