gpu-mode / ring-attentionLinks
ring-attention experiments
☆161Updated 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:
- ☆271Updated last week
- Collection of kernels written in Triton language☆174Updated 9 months ago
- Applied AI experiments and examples for PyTorch☆312Updated 4 months ago
- Cataloging released Triton kernels.☆287Updated 4 months ago
- Triton-based implementation of Sparse Mixture of Experts.☆260Updated 3 months ago
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆310Updated this week
- Fast low-bit matmul kernels in Triton☆423Updated last month
- extensible collectives library in triton☆92Updated 9 months ago
- PyTorch bindings for CUTLASS grouped GEMM.☆140Updated 7 months ago
- This repository contains the experimental PyTorch native float8 training UX☆227Updated last year
- A bunch of kernels that might make stuff slower 😉☆75Updated this week
- Triton-based Symmetric Memory operators and examples☆74Updated this week
- Ship correct and fast LLM kernels to PyTorch☆132Updated this week
- 🚀 Efficiently (pre)training foundation models with native PyTorch features, including FSDP for training and SDPA implementation of Flash…☆278Updated last month
- 🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.☆218Updated this week
- ☆101Updated last year
- Autonomous GPU Kernel Generation via Deep Agents☆211Updated this week
- ☆115Updated last year
- Boosting 4-bit inference kernels with 2:4 Sparsity☆91Updated last year
- ☆178Updated last year
- Accelerating MoE with IO and Tile-aware Optimizations☆542Updated this week
- Load compute kernels from the Hub☆376Updated this week
- Small scale distributed training of sequential deep learning models, built on Numpy and MPI.☆154Updated 2 years ago
- ☆132Updated 7 months ago
- A curated collection of resources, tutorials, and best practices for learning and mastering NVIDIA CUTLASS☆248Updated 8 months ago
- TPU inference for vLLM, with unified JAX and PyTorch support.☆213Updated this week
- a minimal cache manager for PagedAttention, on top of llama3.☆130Updated last year
- Framework to reduce autotune overhead to zero for well known deployments.☆92Updated 3 months ago
- Learn CUDA with PyTorch☆176Updated 3 weeks ago
- A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.☆711Updated this week