Alternatives and similar repositories for triton-viz:

Users that are interested in triton-viz are comparing it to the libraries listed below

• gpu-mode / triton-index
  Cataloging released Triton kernels.
  ☆157Updated 2 weeks ago
• pytorch-labs / applied-ai
  Applied AI experiments and examples for PyTorch
  ☆215Updated last week
• mobiusml / gemlite
  Fast low-bit matmul kernels in Triton
  ☆199Updated last week
• pytorch-labs / float8_experimental
  This repository contains the experimental PyTorch native float8 training UX
  ☆219Updated 5 months ago
• ColfaxResearch / cutlass-kernels
  ☆180Updated 6 months ago
• zinccat / Awesome-Triton-Kernels
  Collection of kernels written in Triton language
  ☆91Updated 3 months ago
• foundation-model-stack / foundation-model-stack
  🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.
  ☆186Updated last week
• cchan / tccl
  extensible collectives library in triton
  ☆77Updated 4 months ago
• gpu-mode / ring-attention
  ring-attention experiments
  ☆119Updated 3 months ago
• pranjalssh / fast.cu
  Fastest kernels written from scratch
  ☆128Updated 2 months ago
• ScalingIntelligence / KernelBench
  KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems
  ☆99Updated last week
• triton-lang / kernels
  ☆64Updated 2 months ago
• pytorch-labs / tritonbench
  Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.
  ☆86Updated this week
• HanGuo97 / flute
  Fast Matrix Multiplications for Lookup Table-Quantized LLMs
  ☆221Updated last week
• BobMcDear / attorch
  A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.
  ☆511Updated this week
• jundaf2 / INT8-Flash-Attention-FMHA-Quantization
  ☆157Updated last year
• andylolu2 / simpleGEMM
  The simplest but fast implementation of matrix multiplication in CUDA.
  ☆34Updated 6 months ago
• usyd-fsalab / fp6_llm
  An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).
  ☆232Updated 3 months ago
• wangsiping97 / FastGEMV
  High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.
  ☆94Updated 6 months ago
• gpu-mode / profiling-cuda-in-torch
  ☆140Updated 11 months ago
• yifuwang / symm-mem-recipes
  ☆36Updated last month
• siboehm / ShallowSpeed
  Small scale distributed training of sequential deep learning models, built on Numpy and MPI.
  ☆116Updated last year
• stanford-futuredata / stk
  ☆97Updated 5 months ago
• 66RING / tiny-flash-attention
  flash attention tutorial written in python, triton, cuda, cutlass
  ☆255Updated 3 weeks ago
• SqueezeBits / QUICK
  QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
  ☆114Updated 10 months ago
• tspeterkim / paged-attention-minimal
  a minimal cache manager for PagedAttention, on top of llama3.
  ☆63Updated 5 months ago
• IST-DASLab / Sparse-Marlin
  Boosting 4-bit inference kernels with 2:4 Sparsity
  ☆64Updated 4 months ago
• shawntan / scattermoe
  Triton-based implementation of Sparse Mixture of Experts.
  ☆194Updated 2 months ago
• mit-han-lab / qserve
  QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
  ☆492Updated this week