---
id: fa3-fwd
version: "0.0.3"
license: unclear
license_treatment: permissive
maintenance: active
---

# fa3-fwd — FlashAttention-3 forward

License: permissive · Maintenance: active · Downloads: 78.9K/mo

## What it is and what it does

fa3-fwd is a minimal Python package that bundles the Flash-Attention-3 forward kernel as a compiled wheel, stripping out backward operators, local attention, paged KV cache, FP16 kernels, and other features unnecessary for inference. It exposes the forward kernel through a renamed interface to avoid conflicts and keep the wheel size small.

The package is built on top of torch, einops, packaging, and ninja. It targets inference scenarios where you need fast attention computation on CUDA but don't need gradient computation or the full feature set of the upstream Flash-Attention project. Installation uses pre-built wheels for Linux aarch64 and x86_64, so setup is typically straightforward on supported platforms.

Use it for:

- Accelerate transformer inference on CUDA by replacing standard attention with optimized Flash-Attention-3 forward pass
- Reduce model serving latency in production by using lightweight attention kernel without backward-pass overhead
- Build inference-only applications where gradient computation is not needed, minimizing dependencies and wheel size
- Deploy language models or vision transformers with faster attention computation on NVIDIA GPUs

## Worth the install?

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

Provides a lightweight Flash-Attention-3 forward-only kernel compiled to a Python wheel, optimized for inference workloads on CUDA hardware without backward pass or optional features.

Yes, if you are running inference on CUDA and your platform matches the pre-built wheels (Linux aarch64 or x86_64). The forward-only design keeps the package lean and avoids unnecessary dependencies. Active maintenance and zero known vulnerabilities are positive signals. No if you need backward pass support or are not on a supported platform—use the upstream Flash-Attention project instead.

## Install

pip install fa3-fwd
uv add fa3-fwd
poetry add fa3-fwd

## Installing fa3-fwd

Before you install: Medium install friction due to compiled wheel dependencies on torch, ninja, and packaging. Maintenance status is active. Wheels are pre-built for aarch64 and x86_64 on manylinux_2_24, so installation itself is straightforward once the platform matches.

License in practice: Licensed under Apache Software License (permissive), so you can use this in commercial and proprietary projects without copyleft obligations.

Quickstart:

pip install fa3-fwd

import torch
from fa3_fwd import flash_attn_func

out = flash_attn_func(q, k, v, causal=True)

Requires CUDA-capable GPU, PyTorch 2.10, and Python 3.8 or later. Inputs must already be on CUDA device and satisfy Flash-Attention-3 constraints.

Verify before relying:

- Whether pre-built wheels cover all target platforms or if source builds are needed for other architectures
- Performance characteristics compared to full Flash-Attention or other inference-only attention implementations
- Exact compatibility matrix between PyTorch versions and this package

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 78.9K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

flash attention inference kernel, lightweight attention forward pass, cuda attention optimization, flash attention 3 forward only, inference attention kernel, optimized transformer attention, flash attn forward kernel, cuda-optimization, inference-kernel, transformer-attention

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