Alternatives and similar repositories for gpu_kernels

Users that are interested in gpu_kernels are comparing it to the libraries listed below

• triton-lang / kernels
  ☆88Updated 10 months ago
• IBM / triton-dejavu
  Framework to reduce autotune overhead to zero for well known deployments.
  ☆82Updated this week
• IST-DASLab / Sparse-Marlin
  Boosting 4-bit inference kernels with 2:4 Sparsity
  ☆82Updated last year
• SqueezeBits / QUICK
  QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
  ☆118Updated last year
• IST-DASLab / qutlass
  QuTLASS: CUTLASS-Powered Quantized BLAS for Deep Learning
  ☆92Updated last week
• tgale96 / grouped_gemm
  PyTorch bindings for CUTLASS grouped GEMM.
  ☆119Updated 3 months ago
• stanford-futuredata / stk
  ☆111Updated last year
• microsoft / AttentionEngine
  ☆96Updated 4 months ago
• microsoft / TileFusion
  TileFusion is an experimental C++ macro kernel template library that elevates the abstraction level in CUDA C for tile processing.
  ☆97Updated 2 months ago
• ademeure / DeeperGEMM
  DeeperGEMM: crazy optimized version
  ☆70Updated 4 months ago
• cchan / tccl
  extensible collectives library in triton
  ☆87Updated 5 months ago
• wangsiping97 / FastGEMV
  High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.
  ☆114Updated last year
• tile-ai / AttentionEngine
  ☆50Updated 4 months ago
• ankan-ban / llama_cu_awq
  llama INT4 cuda inference with AWQ
  ☆54Updated 8 months ago
• INT-FlashAttention2024 / INT-FlashAttention
  ☆82Updated 7 months ago
• deepspeedai / DeepSpeed-Kernels
  ☆74Updated 5 months ago
• tlc-pack / libflash_attn
  Standalone Flash Attention v2 kernel without libtorch dependency
  ☆110Updated last year
• feifeibear / ChituAttention
  Quantized Attention on GPU
  ☆44Updated 10 months ago
• opengear-project / GEAR
  GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM
  ☆168Updated last year
• meta-pytorch / tritonbench
  Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.
  ☆223Updated this week
• Bruce-Lee-LY / decoding_attention
  Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.
  ☆43Updated 3 months ago
• jundaf2 / INT8-Flash-Attention-FMHA-Quantization
  ☆159Updated 2 years ago
• InternLM / turbomind
  ☆95Updated 5 months ago
• feifeibear / Odysseus-Transformer
  Odysseus: Playground of LLM Sequence Parallelism
  ☆77Updated last year
• li-plus / flash-preference
  Accelerate LLM preference tuning via prefix sharing with a single line of code
  ☆43Updated 2 months ago
• Qualcomm-AI-research / gptvq
  ☆33Updated last year
• Dao-AILab / grouped-latent-attention
  ☆126Updated 3 months ago
• usyd-fsalab / fp6_llm
  An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).
  ☆265Updated 2 months ago
• zinccat / Awesome-Triton-Kernels
  Collection of kernels written in Triton language
  ☆154Updated 5 months ago
• meta-pytorch / kraken
  Triton-based Symmetric Memory operators and examples
  ☆28Updated this week