---
license: apache-2.0
tags:
  - sageattention
  - flash-attention
  - flash-attn
  - blackwell
  - sm_120
  - rtx-pro-6000
  - rtx-5090
  - cuda-13
  - pytorch-2.11
  - python-3.12
  - wheels
  - prebuilt
  - comfyui
language:
  - en
---

# 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.

---

## Compatibility

| Requirement | Value | Why it matters |
|---|---|---|
| Python | **3.12.x** (built on 3.12.3) | `cp312` ABI tag — won't load on 3.11 or 3.13 |
| PyTorch | **2.11.0+cu130** | C++ ABI must match — won't load with `cu126` torch |
| CUDA toolkit | 13.0 (driver compatible with cu130) | required for runtime |
| GPU compute capability | **12.0 (Blackwell)** — RTX PRO 6000 Blackwell, RTX 5090/5080/5070 Ti, B100/B200, etc. | wheels compiled with `arch=compute_120,code=sm_120` natively (no PTX-JIT fallback) |
| OS | Linux x86_64 | `manylinux` not applied — built on Ubuntu, untested elsewhere |

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

---

## Files & hashes

| File | Size | SHA256 |
|---|---|---|
| `sageattention-2.2.0-cp312-cp312-linux_x86_64.whl` | 14.8 MB | `4a47a462059bf09b7d10be68f93a0a462dd6184068298fc6ff10e94751fa6dd4` |
| `flash_attn-2.8.3.post1-cp312-cp312-linux_x86_64.whl` | 65.8 MB | `a77fe499d1c7244e0549d9d2700def0c1c82a1b629d993204e9be78debd96cb8` |

Verify before installing:
```bash
sha256sum sageattention-2.2.0-cp312-cp312-linux_x86_64.whl
sha256sum flash_attn-2.8.3.post1-cp312-cp312-linux_x86_64.whl
```

---

## Install

```bash
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:
```python
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')
```

---

## 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:**
```bash
TORCH_CUDA_ARCH_LIST="12.0" MAX_JOBS=4 NVCC_THREADS=2 \
  pip install . --no-build-isolation
```

**flash-attn:**
```bash
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.

---

## Licenses

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

- **SageAttention** — [Apache License 2.0](https://github.com/thu-ml/SageAttention/blob/main/LICENSE)
- **FlashAttention** — [BSD 3-Clause](https://github.com/Dao-AILab/flash-attention/blob/main/LICENSE)

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

---

## 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.

---

## 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.