AI/ML
Deploy DeepSeek-R1 on a Cloud Server Using an Ollama Docker Container Step by Step Guide
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Free Installation Guide - Step by Step Instructions Inside!
We are installing the DeepSeek-R1 Model on a GCP n1-standard-4 instance with 1 NVIDIA T4 GPU. Let's see the step by step process.
Step 1: Install NVIDIA GPU Drivers & CUDA on GCP VM
Since we selected a T4 GPU, we must install NVIDIA drivers and CUDA for GPU acceleration.
Update Systemsudo apt update && sudo apt upgrade -y
Install NVIDIA Driver
sudo apt install -y nvidia-driver-535
Check if the GPU is detected:nvidia-smi
The output will show NVIDIA T4 with its memory details.
- Install CUDA & cuDNN
Install CUDA Toolkit 12.2 (Latest)
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pinsudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600wget https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda-repo-ubuntu2204-12-2-local_12.2.0-1_amd64.debsudo dpkg -i cuda-repo-ubuntu2204-12-2-local_12.2.0-1_amd64.debsudo cp /var/cuda-repo-ubuntu2204-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/sudo apt updatesudo apt install -y cuda
Set CUDA paths:
echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrcecho 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrcsource ~/.bashrc
Verify installation:
nvcc --version
Output will show a CUDA version like 12.2.
Step 2: Install Docker & NVIDIA Container Toolkit
- Install Docker
sudo apt install -y docker.iosudo systemctl start dockersudo systemctl enable docker
Verify installation:docker --version
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.listsudo apt updatesudo apt install -y nvidia-docker2sudo systemctl restart docker
Verify GPU support in Docker:docker run --rm --gpus all nvidia/cuda:12.2.0-base nvidia-smi
If it shows the NVIDIA T4 GPU, then we are ready to proceed.
Step 3: Install Ollama in Docker
Now, install Ollama inside a Docker container.
- Pull Ollama Docker Image
docker pull ollama/ollamaStart Ollama with GPU:docker run --rm --gpus all -d --name ollama -p 11434:11434 ollama/ollama
Verify it's running:docker ps
Step 4: Pull & Run Deepseek-r1 8B
Once Ollama is running, pull the Deepseek-r1 8B model:docker exec -it ollama ollama pull deepseek-r1:8b
Wait for it to download.
Run Deepseek-r1:docker exec -it ollama ollama run deepseek-r1:8b
Test it:
curl -X POST "http://localhost:11434/api/generate" -d '{ "model": "deepseek-r1:8b", "prompt": "What is the capital of France?", "stream": false}'
It should return "Paris" as the response.
Step 5: Install & Start Ollama GUI
- Pull Ollama GUI Docker Image
docker pull ghcr.io/open-webui/open-webui:main
Run it:
docker run -d --name ollama-gui -p 3000:3000 \ -e OLLAMA_API_BASE_URL=http://host.docker.internal:11434 \ ghcr.io/open-webui/open-webui:main
This will start Ollama GUI on port 3000.
Step 6: Access Ollama GUI & Use Deepseek-r1
Open http://YOUR_VM_EXTERNAL_IP:3000 in your browser.
You should see the Ollama GUI dashboard.
Click Models and you should see deepseek-r1:8b.
Click Chat, select the model and start chatting.
Final Summary
- Installed NVIDIA GPU drivers, CUDA and cuDNN
- Set up Docker & NVIDIA container support
- Ran Ollama in Docker with GPU acceleration
- Pulled & tested Deepseek-r1 8b model
- Installed Ollama GUI and accessed it in a browser
GCP VM with T4 GPU can now run Deepseek-r1 8B smoothly.
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