Alternatives and similar repositories for x-attention

Users that are interested in x-attention are comparing it to the libraries listed below

• mit-han-lab / Block-Sparse-Attention
  A sparse attention kernel supporting mix sparse patterns
  ☆280Updated 6 months ago
• ByteDance-Seed / FlexPrefill
  Code for paper: [ICLR2025 Oral] FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference
  ☆133Updated 3 months ago
• XunhaoLai / native-sparse-attention-triton
  Efficient triton implementation of Native Sparse Attention.
  ☆208Updated 3 months ago
• attention-survey / Efficient_Attention_Survey
  Efficient Attention Methods: A Comprehensive Survey
  ☆75Updated this week
• z-lab / sparselora
  [ICML 2025] SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity
  ☆51Updated last month
• NVlabs / Fast-dLLM
  Official implementation of "Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding"
  ☆374Updated this week
• NVlabs / COAT
  [ICLR 2025] COAT: Compressing Optimizer States and Activation for Memory-Efficient FP8 Training
  ☆227Updated 2 weeks ago
• andy-yang-1 / DoubleSparse
  16-fold memory access reduction with nearly no loss
  ☆104Updated 4 months ago
• Aaronhuang-778 / Mixture-Compressor-MoE
  [ICLR 2025] Mixture Compressor for Mixture-of-Experts LLMs Gains More
  ☆50Updated 6 months ago
• fla-org / flame
  🔥 A minimal training framework for scaling FLA models
  ☆228Updated last week
• ThisisBillhe / ZipCache
  [NeurIPS 2024] The official implementation of ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification
  ☆24Updated 4 months ago
• Dao-AILab / grouped-latent-attention
  ☆123Updated 2 months ago
• mit-han-lab / Quest
  [ICML 2024] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference
  ☆321Updated last month
• FasterDecoding / TEAL
  ☆139Updated 6 months ago
• thu-nics / MoA
  [CoLM'25] The official implementation of the paper <MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression>
  ☆145Updated last month
• microsoft / SeerAttention
  SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs
  ☆147Updated 2 weeks ago
• horseee / dKV-Cache
  ☆89Updated 3 months ago
• shadowpa0327 / Palu
  [ICLR 2025] Palu: Compressing KV-Cache with Low-Rank Projection
  ☆130Updated 6 months ago
• maomaocun / dLLM-cache
  Official PyTorch implementation of the paper "dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching" (dLLM-Cache…
  ☆137Updated 2 weeks ago
• HanGuo97 / log-linear-attention
  ☆237Updated 2 months ago
• thu-nics / DiTFastAttn
  ☆173Updated 7 months ago
• tilde-research / nsa-impl
  An efficient implementation of the NSA (Native Sparse Attention) kernel
  ☆113Updated 2 months ago
• thu-nics / ViDiT-Q
  [ICLR'25] ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation
  ☆112Updated 5 months ago
• hao-ai-lab / Awesome-Video-Attention
  A curated list of recent papers on efficient video attention for video diffusion models, including sparsification, quantization, and cach…
  ☆35Updated last month
• sandyresearch / chipmunk
  🎬 3.7× faster video generation E2E 🖼️ 1.6× faster image generation E2E ⚡ ColumnSparseAttn 9.3× vs FlashAttn‑3 💨 ColumnSparseGEMM 2.5× …
  ☆81Updated last month
• Hsu1023 / DuQuant
  [NeurIPS 2024 Oral🔥] DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs.
  ☆165Updated 10 months ago
• alexzhang13 / flashattention2-custom-mask
  Triton implementation of FlashAttention2 that adds Custom Masks.
  ☆132Updated last year
• opengear-project / GEAR
  GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM
  ☆165Updated last year
• ChenMnZ / PrefixQuant
  An algorithm for weight-activation quantization (W4A4, W4A8) of LLMs, supporting both static and dynamic quantization
  ☆147Updated 3 months ago
• ruikangliu / Quantized-Reasoning-Models
  [COLM 2025] Official PyTorch implementation of "Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models"
  ☆43Updated last month