INT-FlashAttention2024 / INT-FlashAttentionLinks
☆79Updated 6 months ago
Alternatives and similar repositories for INT-FlashAttention
Users that are interested in INT-FlashAttention are comparing it to the libraries listed below
Sorting:
- ☆51Updated last year
- A GPU-optimized system for efficient long-context LLMs decoding with low-bit KV cache.☆56Updated this week
- SpInfer: Leveraging Low-Level Sparsity for Efficient Large Language Model Inference on GPUs☆50Updated 4 months ago
- QuTLASS: CUTLASS-Powered Quantized BLAS for Deep Learning☆57Updated 3 weeks ago
- [ICML 2025] Official PyTorch implementation of "FlatQuant: Flatness Matters for LLM Quantization"☆151Updated 2 weeks ago
- LLM Inference with Microscaling Format☆25Updated 8 months ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆113Updated last year
- An algorithm for weight-activation quantization (W4A4, W4A8) of LLMs, supporting both static and dynamic quantization☆145Updated 2 months ago
- PyTorch bindings for CUTLASS grouped GEMM.☆107Updated 2 months ago
- ☆60Updated 3 months ago
- [MLSys'24] Atom: Low-bit Quantization for Efficient and Accurate LLM Serving☆318Updated last year
- Implement Flash Attention using Cute.☆92Updated 7 months ago
- Quantized Attention on GPU☆44Updated 8 months ago
- ☆150Updated last year
- ☆64Updated last year
- Fast Hadamard transform in CUDA, with a PyTorch interface☆213Updated last year
- [ACL 2024] A novel QAT with Self-Distillation framework to enhance ultra low-bit LLMs.☆117Updated last year
- QQQ is an innovative and hardware-optimized W4A8 quantization solution for LLMs.☆137Updated 4 months ago
- DeeperGEMM: crazy optimized version☆71Updated 3 months ago
- QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference☆118Updated last year
- ☆75Updated 2 months ago
- Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity☆216Updated last year
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆40Updated last month
- llama INT4 cuda inference with AWQ☆54Updated 6 months ago
- Code implementation of GPTAQ (https://arxiv.org/abs/2504.02692)☆55Updated last week
- AFPQ code implementation☆22Updated last year
- Code Repository of Evaluating Quantized Large Language Models☆129Updated 11 months ago
- ☆96Updated 10 months ago
- GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM☆165Updated last year
- 16-fold memory access reduction with nearly no loss☆103Updated 4 months ago