jundaf2 / INT8-Flash-Attention-FMHA-Quantization
☆156Updated last year
Related projects ⓘ
Alternatives and complementary repositories for INT8-Flash-Attention-FMHA-Quantization
- This repository contains integer operators on GPUs for PyTorch.☆183Updated last year
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆90Updated 4 months ago
- Applied AI experiments and examples for PyTorch☆166Updated 3 weeks ago
- ☆167Updated 4 months ago
- This repository contains the experimental PyTorch native float8 training UX☆211Updated 3 months ago
- ☆88Updated 2 months ago
- Simple and fast low-bit matmul kernels in CUDA / Triton☆143Updated this week
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆208Updated 3 weeks ago
- A collection of memory efficient attention operators implemented in the Triton language.☆219Updated 5 months ago
- Fast Hadamard transform in CUDA, with a PyTorch interface☆111Updated 5 months ago
- ☆46Updated 2 months ago
- Reorder-based post-training quantization for large language model☆182Updated last year
- PyTorch bindings for CUTLASS grouped GEMM.☆53Updated 2 weeks ago
- ☆122Updated last year
- Cataloging released Triton kernels.☆134Updated 2 months ago
- Triton-based implementation of Sparse Mixture of Experts.☆185Updated last month
- Fast Matrix Multiplications for Lookup Table-Quantized LLMs☆187Updated this week
- [MLSys'24] Atom: Low-bit Quantization for Efficient and Accurate LLM Serving☆278Updated 4 months ago
- QQQ is an innovative and hardware-optimized W4A8 quantization solution for LLMs.☆87Updated last month
- Standalone Flash Attention v2 kernel without libtorch dependency☆98Updated 2 months ago
- Patch convolution to avoid large GPU memory usage of Conv2D☆79Updated 5 months ago
- ☆152Updated this week
- An easy-to-use package for implementing SmoothQuant for LLMs☆83Updated 6 months ago
- PyTorch extension for emulating FP8 data formats on standard FP32 Xeon/GPU hardware.☆100Updated 11 months ago
- Official repository for LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers☆195Updated 3 months ago
- llama INT4 cuda inference with AWQ☆48Updated 4 months ago
- ☆79Updated 2 months ago
- QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference☆112Updated 8 months ago
- extensible collectives library in triton☆71Updated last month
- Official PyTorch implementation of FlatQuant: Flatness Matters for LLM Quantization☆63Updated last week