---
license: mit
---

# utils wheels for RTX5090

As title, lot of third party librarys which are important for running NN lack prebuilt wheel for RTX5090, so I decide to compile them bymyself and upload them here.

***Use At Your Own Risks, Check Official Release To See if There Are Any Official Supports On Your HW Regularly***

### Inlcuded Library
* Flash Attention
* Xformers (with cutlass/flash attention built-in)
* NATTEN
* SageAttention
* vLLM

### IMPORTANT NOTE
1. I only ensure those wheels can works on RTX50 series (sm120) GPUs, if your platform is mixed with different sm/cu arch GPUs, you may still need to compile them by yourself
2. Env
    * Pytorch: 2.7.0
    * CUDA: 12.8(12.8.1)
    * CUDNN: 9.8
    * Compiler: GCC13
    * Tested platform: Ubuntu 22.04 and 24.04
    * CPU arch: amd64 (x86-64)
3. Not all the wheels are fully functional (due to deps things or source implementation), for example, cutlass w8a8 scaled mm is not working in vllm, you need to use `VLLM_TEST_FORCE_FP8_MARLIN=1` to make VLLM works normally with fp8 weight quantization. If you are using flash attention, you need `VLLM_FLASH_ATTN_VERSION=2` to make it work on 5090
4. If you meet any problem or need wheels for specific setup you can open discussion, but I can't ensure I will do it or not.

### Tips
* Install `triton==3.3.1` for better RTX50 series support
* Install `nvidia-nccl-cu12==2.26.5` for correct multi-gpu deps for RTX50 series
  * Torch 2.7.0 use 2.26.2 in their requirements which is not compatibile with RTX50 series, you should install this from pypi directly with `pip isntall nvidia-nccl-cu12>2.26.2`
* I build all those wheel with `python -m build -n -w .` which is more suitable in modern python packaging, I recommend all the user who want to compile those wheel by themselves follow this scheme. (No matter the project use pyproject or setup.py, build package will works)