# RTX 5080 (Blackwell) GPU Support ✅

## Good News!

The NVIDIA GeForce RTX 5080 uses the Blackwell architecture with compute capability **sm_120** (12.0). **PyTorch nightly builds with CUDA 12.8+ now support RTX 5080!**

## Current Status

- **GPU Model**: NVIDIA GeForce RTX 5080
- **Compute Capability**: sm_120 (12.0)
- **Required CUDA Version**: 12.8+
- **Required PyTorch**: Nightly builds with CUDA 12.8
- **Support Status**: ✅ **Supported** (via nightly builds)

## Automatic Installation

Our `setup.py` script automatically detects RTX 5080 and installs the correct PyTorch version:

```bash
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Run smart installer (automatically installs PyTorch nightly for RTX 5080)
python setup.py
```

The script will:
1. 🔍 Detect your RTX 5080 GPU
2. 📦 Install PyTorch nightly with CUDA 12.8 support
3. ✅ Verify GPU compatibility
4. 🚀 Enable full GPU acceleration

## Running the Application

After installation, just run:

```bash
python app.py
```

You'll see:
```
✅ Detected Blackwell GPU (NVIDIA GeForce RTX 5080)
   Installing PyTorch nightly with CUDA 12.8 support (sm_120 compatible)
🖥️  Local - GPU (NVIDIA GeForce RTX 5080)
📍 Using device: cuda
```

## Manual Installation (Alternative)

If you prefer manual installation:

```bash
# Uninstall existing PyTorch
pip uninstall torch torchvision torchaudio -y

# Install PyTorch nightly with CUDA 12.8
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu128
```

## Verification

Check if your RTX 5080 is working:

```python
import torch
print(f"PyTorch: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU name: {torch.cuda.get_device_name(0)}")
print(f"Compute capability: {torch.cuda.get_device_capability(0)}")
```

Expected output:
```
PyTorch: 2.7.0.dev20250310+cu128
CUDA available: True
GPU name: NVIDIA GeForce RTX 5080
Compute capability: (12, 0)
```

## Alternative Solutions

### 1. Build PyTorch from Source (Advanced)
```bash
# Clone PyTorch
git clone --recursive https://github.com/pytorch/pytorch
cd pytorch

# Set CUDA architecture flags
export TORCH_CUDA_ARCH_LIST="12.0"
export CUDA_HOME=/usr/local/cuda

# Build (takes 1-2 hours)
python setup.py develop
```

**Note**: This is time-consuming and may not work until PyTorch officially adds sm_120 support.

### 2. Use Older GPU (Temporary)
If available, use an older GPU (RTX 40xx, 30xx, etc.) that has compute capability ≤ 9.0.

### 3. Wait for Official Support
The most practical approach is to use CPU mode until PyTorch adds official support.

## Performance Notes

**CPU Mode Performance**:
- Inference is slower but functional
- Small models (< 1B parameters): Acceptable
- Large models (> 7B parameters): Very slow
- Consider using smaller models for now

## Questions?

Check PyTorch compatibility:
```bash
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}'); print(f'Compute capability: {torch.cuda.get_device_capability(0) if torch.cuda.is_available() else \"N/A\"}')"
```