Alternatives and similar repositories for applied-ai:

Users that are interested in applied-ai are comparing it to the libraries listed below

• mobiusml / gemlite
  Fast low-bit matmul kernels in Triton
  ☆236Updated this week
• Deep-Learning-Profiling-Tools / triton-viz
  ☆179Updated last week
• pytorch-labs / float8_experimental
  This repository contains the experimental PyTorch native float8 training UX
  ☆221Updated 6 months ago
• foundation-model-stack / foundation-model-stack
  🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.
  ☆187Updated this week
• gpu-mode / triton-index
  Cataloging released Triton kernels.
  ☆168Updated last month
• ColfaxResearch / cutlass-kernels
  ☆181Updated 7 months ago
• usyd-fsalab / fp6_llm
  An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).
  ☆234Updated 3 months ago
• HanGuo97 / flute
  Fast Matrix Multiplications for Lookup Table-Quantized LLMs
  ☆229Updated this week
• cchan / tccl
  extensible collectives library in triton
  ☆83Updated 4 months ago
• zinccat / Awesome-Triton-Kernels
  Collection of kernels written in Triton language
  ☆105Updated this week
• stanford-futuredata / stk
  ☆100Updated 5 months ago
• wangsiping97 / FastGEMV
  High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.
  ☆97Updated 7 months ago
• yifuwang / symm-mem-recipes
  ☆44Updated last month
• efeslab / Atom
  [MLSys'24] Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
  ☆295Updated 7 months ago
• microsoft / vattention
  Dynamic Memory Management for Serving LLMs without PagedAttention
  ☆290Updated this week
• ScalingIntelligence / KernelBench
  KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems
  ☆183Updated this week
• RulinShao / LightSeq
  Official repository for LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers
  ☆205Updated 6 months ago
• pranjalssh / fast.cu
  Fastest kernels written from scratch
  ☆170Updated this week
• jundaf2 / INT8-Flash-Attention-FMHA-Quantization
  ☆157Updated last year
• FlagOpen / FlagAttention
  A collection of memory efficient attention operators implemented in the Triton language.
  ☆237Updated 8 months ago
• foundation-model-stack / fms-fsdp
  🚀 Efficiently (pre)training foundation models with native PyTorch features, including FSDP for training and SDPA implementation of Flash…
  ☆222Updated this week
• pytorch-labs / tritonbench
  Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.
  ☆89Updated this week
• triton-lang / kernels
  ☆67Updated 3 months ago
• spcl / QuaRot
  Code for Neurips24 paper: QuaRot, an end-to-end 4-bit inference of large language models.
  ☆342Updated 2 months ago
• Dao-AILab / fast-hadamard-transform
  Fast Hadamard transform in CUDA, with a PyTorch interface
  ☆143Updated 8 months ago
• shawntan / scattermoe
  Triton-based implementation of Sparse Mixture of Experts.
  ☆196Updated 2 months ago
• sail-sg / zero-bubble-pipeline-parallelism
  Zero Bubble Pipeline Parallelism
  ☆336Updated last week
• fanshiqing / grouped_gemm
  PyTorch bindings for CUTLASS grouped GEMM.
  ☆94Updated last month
• tgale96 / grouped_gemm
  PyTorch bindings for CUTLASS grouped GEMM.
  ☆64Updated 3 months ago