sageattention-flashattn

SageAttention 2.2 + FlashAttention 2.8.3 — Linux wheels for Blackwell (sm_120) on PyTorch 2.11 / CUDA 13.0

Community-built .whl files for a combo that, as of June 2026, isn't shipped by upstream:

• SageAttention 2.2.0 — only Windows wheels in upstream releases; Linux requires source build
• FlashAttention 2.8.3.post1 — Dao-AILab releases don't cover cu130 + torch 2.11 yet

If your hardware/software matches the table below, these install in seconds instead of 5–40 minutes of source compilation.

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Compatibility

If any row doesn't match — these wheels probably won't load and you'll need to build from source.

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Files & hashes

Verify before installing:

sha256sum sageattention-2.2.0-cp312-cp312-linux_x86_64.whl
sha256sum flash_attn-2.8.3.post1-cp312-cp312-linux_x86_64.whl

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Install

pip install sageattention-2.2.0-cp312-cp312-linux_x86_64.whl
pip install flash_attn-2.8.3.post1-cp312-cp312-linux_x86_64.whl

Smoke test:

import torch
import sageattention
from sageattention import sageattn
from flash_attn import flash_attn_func

q = torch.randn(2, 8, 256, 64, dtype=torch.float16, device='cuda')
k = torch.randn(2, 8, 256, 64, dtype=torch.float16, device='cuda')
v = torch.randn(2, 8, 256, 64, dtype=torch.float16, device='cuda')

# sageattention expects (B, H, S, D) by default with tensor_layout='HND'
out_sage = sageattn(q, k, v, tensor_layout='HND', is_causal=False)

# flash_attn_func expects (B, S, H, D)
q2 = q.transpose(1, 2).contiguous()
k2 = k.transpose(1, 2).contiguous()
v2 = v.transpose(1, 2).contiguous()
out_flash = flash_attn_func(q2, k2, v2, causal=False)

print('sageattention:', out_sage.shape, '— OK')
print('flash_attn:   ', out_flash.shape, '— OK')

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Build environment (exactly what produced these wheels)

OS:       Ubuntu 24.04 (kernel 6.14.0-1010-oem)
Python:   3.12.3
PyTorch:  2.11.0+cu130
CUDA:     13.0.r13.0 / V13.0.88 (apt cuda-toolkit-13-0)
ninja:    1.11.1.4
setuptools: 81.0.0
GPU:      NVIDIA RTX PRO 6000 Blackwell Workstation Edition (sm_120)

Build flags:

sageattention:

TORCH_CUDA_ARCH_LIST="12.0" MAX_JOBS=4 NVCC_THREADS=2 \
  pip install . --no-build-isolation

flash-attn:

TORCH_CUDA_ARCH_LIST="12.0" FLASH_ATTN_CUDA_ARCHS="120" MAX_JOBS=2 NVCC_THREADS=1 \
  pip install flash-attn==2.8.3.post1 --no-build-isolation

Backward kernels are included in flash-attn — training works, not just inference.

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Licenses

Both upstream projects allow redistribution; redistributing these binary builds preserves their license terms:

• SageAttention — Apache License 2.0
• FlashAttention — BSD 3-Clause

The wheels contain copies of LICENSE files in their *.dist-info/licenses/ directories. Originals are at:

• https://github.com/thu-ml/SageAttention
• https://github.com/Dao-AILab/flash-attention

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Disclaimers (read these)

• Not official. This is a community build. Authors of upstream packages have no involvement.
• Smoke-tested only. Each wheel was verified to: (1) import without errors, (2) execute a small forward pass on a Blackwell GPU and return a tensor of expected shape and dtype. No accuracy benchmarks, no model-level integration tests were run.
• No warranty. If these wheels misbehave on your system — wrong results, crashes, performance regressions — that's on you to verify against a source build before relying on them.
• Reproducible-ish. Wheel SHA-256 may differ slightly between rebuilds due to embedded timestamps even when binary kernel content is identical.

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Attribution

These wheels were built by JahJedi with assistance from Claude Code (Anthropic) — the diagnosis of an earlier system freeze under unbounded ninja, the choice of safe build parameters (MAX_JOBS, NVCC_THREADS, single-arch targeting), and this README itself were Claude-assisted. Verification commands were run by the human; final responsibility for the published artifacts is the human's.

If anything in these wheels turns out to be wrong, ping the human first — they can't claim AI ignorance.