---
id: fla-core
version: "0.5.2"
license: MIT License Copyright (c) 2023-2026 Songlin Yang, Yu Zhang, Zhiyuan Li Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# fla-core — Core operations for flash-linear-attention

License: permissive · Maintenance: active · Downloads: 696.0K/mo

## What it is and what it does

Flash Linear Attention is a library of optimized building blocks for modern sequence models that move beyond standard quadratic-complexity attention. It bundles hardware-efficient kernels, training-ready layers, and complete model implementations spanning linear attention mechanisms, sparse attention patterns, state space models, and hybrid architectures that mix different attention types. The library targets practitioners building or experimenting with alternatives to Transformers—researchers exploring state-of-the-art sequence modeling papers, engineers deploying models on resource-constrained hardware, and teams training large language models where memory and compute efficiency matter.

The package is organized around fused operations and platform-agnostic implementations verified on NVIDIA, AMD, and Intel hardware. It depends only on einops for tensor manipulation and requires Python 3.10 or later. The description and news section indicate active development with frequent additions of new model variants and backends throughout 2025 and 2026, suggesting this is a living research-to-production bridge rather than a static library.

Use it for:

- Replace standard Transformer attention with linear or sparse alternatives to reduce memory and compute during training or inference of large language models.
- Experiment with state space models as sequence modeling primitives without implementing custom CUDA kernels.
- Train hybrid models that combine multiple attention mechanisms in a single architecture.
- Deploy sequence models on AMD or Intel accelerators where NVIDIA-only libraries are unavailable or suboptimal.
- Benchmark and compare different subquadratic attention schemes on the same hardware and codebase.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides hardware-efficient implementations of linear attention, sparse attention, state space models, and hybrid sequence model architectures, verified across NVIDIA, AMD, and Intel platforms.

Yes. The package is actively maintained, has low install friction, carries a permissive MIT License, and fills a genuine need for practitioners exploring or deploying efficient sequence models. No known security vulnerabilities. Install if you are building or researching linear attention, state space models, or hybrid architectures; skip if you only need standard Transformer attention.

## Install

pip install fla-core
uv add fla-core
poetry add fla-core

## Installing fla-core

Before you install: Low install friction with a single pure-Python dependency (einops). Active maintenance with recent commits and steady releases; repository shows 5560 stars and receives regular updates.

License in practice: MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

Quickstart:

pip install fla-core
import fla
# Use a model implementation or kernel from fla.layers or fla.ops

Requires Python 3.10 or later; hardware acceleration benefits depend on NVIDIA, AMD, or Intel accelerator availability.

Verify before relying:

- Whether all advertised model implementations (RetNet, Mamba, GLA, DeltaNet, etc.) are production-ready or research prototypes
- Performance benchmarks and actual speedup claims compared to standard attention implementations
- Compatibility with specific PyTorch or CUDA versions beyond the Python 3.10+ requirement

## Package facts

- License: MIT License Copyright (c) 2023-2026 Songlin Yang, Yu Zhang, Zhiyuan Li Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 696.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

linear attention kernels, efficient sequence models, state space model implementations, hardware-optimized attention, sparse attention layers, transformer alternatives, subquadratic attention, linear-attention, sequence-models, gpu-kernels

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