I Made Pytorch 2.10.0a0 for Sm120

RTX 5080 (sm_120) Throughput Test ============================

Matrix size: 4096x4096 FLOAT32 → 50.90 TFLOPS FLOAT16 → 114.54 TFLOPS BFLOAT16 → 94.76 TFLOPS

Matrix size: 8192x8192 FLOAT32 → 57.98 TFLOPS FLOAT16 → 118.84 TFLOPS BFLOAT16 → 120.16 TFLOPS

=========================== Benchmark completed.

# PyTorch 2.10 for RTX 5080 - Windows 11

## Requirements - Windows 11 - Python 3.10 or 3.11 - NVIDIA GeForce RTX 5080 - Latest NVIDIA drivers (560+)

## Installation

1. Create a virtual environment: ```powershell python -m venv .venv .\.venv\Scripts\Activate.ps1 ```

2. Run the installer: ```powershell .\install.ps1 ```

## Verify Installation ```powershell python -c "import torch; print(torch.cuda.is_available())" ```

## Package Contents - PyTorch 2.10.0a0 (custom build for SM 120 / Blackwell) - CUDA 13.0 runtime libraries - cuDNN support - All required DLL dependencies

## Build Info - Built with CUDA 13.0 - Compute capability: SM 120 (Blackwell) - Build date: [ADD DATE] - Source: PyTorch main branch

## Troubleshooting

If you get DLL errors: 1. Ensure you have the latest NVIDIA drivers 2. Check that Python 3.10 or 3.11 is being used 3. Make sure you're in a clean virtual environment

## License PyTorch is BSD-licensed. See torch/LICENSE for details.

## Verification SHA256: `6202cfa3f4dac89e87bd21b754b3778288849428576e1bfd1dc11de4cfee421d` Verified on: Windows 11 Pro 23H2