meta-pytorch / applied-aiLinks
Applied AI experiments and examples for PyTorch
☆312Updated 4 months ago
Alternatives and similar repositories for applied-ai
Users that are interested in applied-ai are comparing it to the libraries listed below
Sorting:
- Fast low-bit matmul kernels in Triton☆413Updated 2 weeks ago
- Cataloging released Triton kernels.☆280Updated 3 months ago
- ☆268Updated last week
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆308Updated this week
- 🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.☆217Updated 3 weeks ago
- Collection of kernels written in Triton language☆174Updated 8 months ago
- PyTorch bindings for CUTLASS grouped GEMM.☆135Updated 7 months ago
- A Quirky Assortment of CuTe Kernels☆724Updated last week
- This repository contains the experimental PyTorch native float8 training UX☆227Updated last year
- extensible collectives library in triton☆91Updated 9 months ago
- Accelerating MoE with IO and Tile-aware Optimizations☆469Updated last week
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆277Updated 5 months ago
- ☆254Updated last year
- ☆153Updated last year
- Triton-based implementation of Sparse Mixture of Experts.☆259Updated 2 months ago
- A curated collection of resources, tutorials, and best practices for learning and mastering NVIDIA CUTLASS☆243Updated 7 months ago
- A collection of memory efficient attention operators implemented in the Triton language.☆287Updated last year
- ring-attention experiments☆160Updated last year
- ☆99Updated last year
- PyTorch bindings for CUTLASS grouped GEMM.☆177Updated 2 weeks ago
- QuTLASS: CUTLASS-Powered Quantized BLAS for Deep Learning☆153Updated last month
- [MLSys'24] Atom: Low-bit Quantization for Efficient and Accurate LLM Serving☆332Updated last year
- Autonomous GPU Kernel Generation via Deep Agents☆197Updated this week
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆123Updated last year
- ☆115Updated last year
- kernels, of the mega variety☆634Updated 3 months ago
- Best practices for training DeepSeek, Mixtral, Qwen and other MoE models using Megatron Core.☆143Updated 2 weeks ago
- A bunch of kernels that might make stuff slower 😉☆72Updated this week
- Perplexity GPU Kernels☆547Updated last month
- a minimal cache manager for PagedAttention, on top of llama3.☆129Updated last year