gpu-mode / ring-attentionLinks
ring-attention experiments
☆160Updated last year
Alternatives and similar repositories for ring-attention
Users that are interested in ring-attention are comparing it to the libraries listed below
Sorting:
- Cataloging released Triton kernels.☆274Updated 2 months ago
- ☆256Updated this week
- A bunch of kernels that might make stuff slower 😉☆65Updated this week
- Fast low-bit matmul kernels in Triton☆401Updated last week
- Applied AI experiments and examples for PyTorch☆307Updated 3 months ago
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆294Updated this week
- Triton-based implementation of Sparse Mixture of Experts.☆253Updated 2 months ago
- Collection of kernels written in Triton language☆169Updated 7 months ago
- This repository contains the experimental PyTorch native float8 training UX☆226Updated last year
- PyTorch bindings for CUTLASS grouped GEMM.☆131Updated 6 months ago
- 🚀 Efficiently (pre)training foundation models with native PyTorch features, including FSDP for training and SDPA implementation of Flash…☆271Updated last week
- Ship correct and fast LLM kernels to PyTorch☆124Updated 2 weeks ago
- ☆132Updated 6 months ago
- extensible collectives library in triton☆91Updated 8 months ago
- 🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.☆217Updated last week
- a minimal cache manager for PagedAttention, on top of llama3.☆126Updated last year
- Flash-Muon: An Efficient Implementation of Muon Optimizer☆212Updated 5 months ago
- JAX backend for SGL☆185Updated this week
- ☆113Updated last year
- Learn CUDA with PyTorch☆117Updated last week
- Triton-based Symmetric Memory operators and examples☆63Updated last month
- A curated collection of resources, tutorials, and best practices for learning and mastering NVIDIA CUTLASS☆244Updated 6 months ago
- ☆94Updated last year
- Load compute kernels from the Hub☆337Updated last week
- TPU inference for vLLM, with unified JAX and PyTorch support.☆170Updated this week
- Boosting 4-bit inference kernels with 2:4 Sparsity☆86Updated last year
- Autonomous GPU Kernel Generation via Deep Agents☆163Updated last week
- kernels, of the mega variety☆614Updated 2 months ago
- QuTLASS: CUTLASS-Powered Quantized BLAS for Deep Learning☆143Updated 3 weeks ago
- Small scale distributed training of sequential deep learning models, built on Numpy and MPI.☆151Updated 2 years ago