fa3-fwd
FlashAttention-3 forward
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
- Medium install friction due to compiled wheel dependencies on torch, ninja, and packaging.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), so you can use this in commercial and proprietary projects without copyleft obligations.
last release 2026-04-15 (121 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 78,884 downloads/mo, #14,399 on PyPI
Alternatives
Verify before relying
pip install fa3-fwd
import torch
from fa3_fwd import flash_attn_func
out = flash_attn_func(q, k, v, causal=True)- 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
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 on this page. Verify before relying on it.
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
fa3-fwd on PyPI
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.
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.
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)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 | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagestorcheinopspackagingninja |
| Maintenance | Actively maintained 121 days since the last release |
| First released | |
| Downloads | 78,884 / month, #14,399 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: UnixProgramming Language :: Python :: 3 |
Evidence: fa3_fwd-0.0.3-cp39-abi3-manylinux_2_24_aarch64.whl; fa3_fwd-0.0.3-cp39-abi3-manylinux_2_24_x86_64.whl
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See also nvidia-cudnn-frontend · flash-attn · flashinfer-python · flash-attn-4 · ring-flash-attn · sgl-kernel · sglang-kernel · CoLT5-attention · causal-conv1d · flashinfer-cubin