Dual Boot: Windows + Ubuntu Guide

# Check what GPU is detected
lspci | grep -i nvidia

# List recommended drivers
ubuntu-drivers devices

# Example output:
# == /sys/devices/pci0000:00/0000:00:01.0/0000:01:00.0 ==
# vendor   : NVIDIA Corporation
# model    : GeForce RTX 5090
# driver   : nvidia-driver-570 - third-party non-free recommended
# driver   : nvidia-driver-570-server - third-party non-free

# Option A: Auto-install recommended driver (easiest)
sudo ubuntu-drivers autoinstall

# Option B: Install specific version
sudo apt install -y nvidia-driver-570

# Reboot required!
sudo reboot

# Check driver is loaded
nvidia-smi

# Expected output:
# +-----------------------------------------------------------------------------------------+
# | NVIDIA-SMI 570.xx.xx    Driver Version: 570.xx.xx    CUDA Version: 12.8               |
# |-------------------------------+----------------------+----------------------+
# | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
# | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
# |===============================+======================+======================|
# |   0  NVIDIA GeForce RTX 5090  |   00000000:01:00.0   |                  N/A |
# | 30%   35C    P8    25W / 575W |    128MiB / 32768MiB |      0%      Default |
# +-------------------------------+----------------------+----------------------+

# Check kernel module
lsmod | grep nvidia

# Check driver version
cat /proc/driver/nvidia/version

# Create blacklist file
sudo bash -c 'cat > /etc/modprobe.d/blacklist-nouveau.conf << EOF
blacklist nouveau
options nouveau modeset=0
EOF'

# Regenerate initramfs
sudo update-initramfs -u

# Reboot
sudo reboot

# Option A: Via apt (recommended for Ubuntu)
# Add NVIDIA CUDA repository
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2404/x86_64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt update

# Install CUDA Toolkit 12.8
sudo apt install -y cuda-toolkit-12-8

# Add to PATH — append to ~/.bashrc
echo 'export PATH=/usr/local/cuda-12.8/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-12.8/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

# Verify CUDA installation
nvcc --version
# cuda_12.8.x ...

# Install cuDNN via apt (with CUDA repo already added)
sudo apt install -y libcudnn9-cuda-12 libcudnn9-dev-cuda-12

# Verify cuDNN
dpkg -l | grep cudnn

# Add NVIDIA Container Toolkit repository
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey |   sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list |   sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' |   sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt update
sudo apt install -y nvidia-container-toolkit

# Configure Docker runtime
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

# Verify GPU access in Docker
docker run --rm --gpus all nvidia/cuda:12.8.0-base-ubuntu24.04 nvidia-smi

# Test PyTorch GPU access
python3 -c "
import torch
print(f'PyTorch version: {torch.__version__}')
print(f'CUDA available: {torch.cuda.is_available()}')
print(f'CUDA version: {torch.version.cuda}')
print(f'GPU count: {torch.cuda.device_count()}')
print(f'GPU name: {torch.cuda.get_device_name(0)}')
print(f'GPU memory: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB')
"

# Test vLLM serving (Docker)
docker run --gpus all   -v ~/.cache/huggingface:/root/.cache/huggingface   -p 8000:8000   --ipc=host   vllm/vllm-openai:latest   --model meta-llama/Llama-3.1-8B-Instruct   --max-model-len 8192

# Real-time GPU monitoring (updates every 1 second)
watch -n 1 nvidia-smi

# GPU monitoring with processes
nvidia-smi pmon -i 0 -s um -d 1

# Install nvitop for better monitoring
pip install nvitop
nvitop