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Native Sparse Attention Triton

Native Sparse Attention Triton is an Apache-2.0-licensed Python library that implements Native Sparse Attention with Triton for LLM training and inference, including variable-length prefill, decoding and KV-cache management.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Native Sparse Attention Triton — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when building or experimenting with a PyTorch-based LLM that needs Native Sparse Attention operators or a toy model for training and generation.

Who it is for

  • LLM developers integrating sparse attention into training pipelines.
  • Researchers and engineers prototyping variable-length inference or experimenting with sparse-attention models.

Documented uses

  • Use sparse-attention operators for model training.
  • Prototype inference with prefill and decoding over variable-length inputs.
  • Explore KV-cache management and sparse attention through the included ToyNSALlama model.
Before you choose

Requirements & limits

The project is publicly available on GitHub under the Apache-2.0 license. Installation is documented through pip from the GitHub repository.

  • Requires PyTorch >= 2.1.0, triton >= 3.0.0, einops >= 0.7.0 and flash_attn >= 2.6.3.
  • Variable-length batch inputs must be concatenated before use.
  • Supported attention head dimensions are less than 128.
  • The README says PyTorch operators are intended for debugging and recommends Triton operators for production use.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the library from GitHub

Install the Python package from the repository using the documented pip command.

Before you start

  • PyTorch >= 2.1.0
  • triton >= 3.0.0
  • einops >= 0.7.0
  • flash_attn >= 2.6.3

Steps

  1. Install the listed dependencies.

  2. Run the command below.

    Documented command
    pip install git+https://github.com/XunhaoLai/native-sparse-attention-triton.git

Expected outcome

The `native_sparse_attention` package is installed from the GitHub repository.

Guide limitations

  • This documents installation only; it does not cover configuring a model or running inference.
  • The README states that supported attention head dimensions are less than 128.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Native Sparse Attention TritonUse sparse-attention operators for model training.Cloud, Linux, ServerThe project is publicly available on GitHub under the Apache-2.0 license. Installation is documented through pip from the GitHub repository.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
XunhaoLai/native-sparse-attention-triton · Official homepage title · retained in its source language
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