Deploying DeepSeek-R1 14B on Linux requires a capable hardware setup and a properly configured software stack. This guide will cover hardware requirements, installation steps, and optimizations to ensure smooth operation.

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1. Hardware Requirements

Component	Minimum Requirement	Recommended Requirement
CPU	AMD Ryzen 7 / Intel i7	AMD Ryzen 9 / Intel i9
RAM	32GB DDR4	64GB+ DDR5
GPU	1x NVIDIA RTX 3090 (24GB VRAM)	1x NVIDIA RTX 4090 / A6000 (48GB VRAM)
VRAM	16GB minimum	24GB+ recommended
Storage	512GB NVMe SSD	1TB+ NVMe SSD (PCIe 4.0)
Power Supply	750W+	850W+
Cooling	Standard air cooling	Water cooling for GPUs

Note: The DeepSeek-R1 14B model can run on a single 16GB+ GPU, but performance is better with 24GB+ VRAM.

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2. Install Required Software

Step 1: Update Linux and Install Dependencies

sudo apt update && sudo apt upgrade -y
sudo apt install -y build-essential git curl wget python3 python3-pip

Step 2: Install NVIDIA Drivers, CUDA & cuDNN

1. Install NVIDIA Drivers

sudo apt install -y nvidia-driver-535
reboot

Verify installation:

nvidia-smi

2. Install CUDA 12.3

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-repo-ubuntu2204_12.3.0-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu2204_12.3.0-1_amd64.deb
sudo apt update
sudo apt install -y cuda

3. Install cuDNN

sudo apt install -y libcudnn8 libcudnn8-dev

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3. Install Docker & NVIDIA Container Toolkit

Step 1: Install Docker

sudo apt install -y docker.io
sudo systemctl enable docker
sudo systemctl start docker

Step 2: Install NVIDIA Container Toolkit

sudo apt install -y nvidia-container-toolkit
sudo systemctl restart docker

Step 3: Verify NVIDIA Docker Support

docker run --rm --gpus all nvidia/cuda:12.3.0-base nvidia-smi

If successful, you should see your NVIDIA GPUs listed.

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4. Install Ollama & Pull DeepSeek-R1 14B

Step 1: Install Ollama

curl -fsSL https://ollama.ai/install.sh | sh

Verify installation:

ollama --version

Step 2: Pull the DeepSeek-R1 14B Model

ollama pull deepseek/deepseek-r1-14b

This is a large model (~1.2TB), ensure you have sufficient storage.

Step 3: Run DeepSeek-R1 14B with Ollama

ollama run deepseek/deepseek-r1-14b

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5. Set Up WebUI for DeepSeek-R1 14B

Step 1: Clone WebUI Repository

git clone https://github.com/deepseek-ai/webui.git
cd webui

Step 2: Build & Run WebUI with Docker

1. Build WebUI Docker Image

   docker build -t deepseek-webui .

2. Run WebUI Container

   docker run --gpus all --shm-size=128G -p 7860:7860 -v deepseek_cache:/root/.cache deepseek-webui

--shm-size=128G increases shared memory for better model execution.

Step 3: Access WebUI

• Open your browser and go to:

  http://localhost:7860

• Now, you can interact with DeepSeek-R1 14B via WebUI.

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6. Performance Optimization

Enable Multi-GPU Scaling (NCCL)

export NCCL_P2P_DISABLE=0
export NCCL_IB_DISABLE=0
export NCCL_DEBUG=INFO

Allocate More Memory to Docker

docker run --gpus all --shm-size=256G -p 7860:7860 deepseek-webui

Run in Background with Logs

nohup ollama run deepseek/deepseek-r1-14b > deepseek.log 2>&1 &

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7. Conclusion

You have successfully set up DeepSeek-R1 14B on Linux using Ollama, Docker, and WebUI.

Next Steps:

• Monitor GPU performance using nvidia-smi
• Optimize memory allocation for better efficiency
• Experiment with smaller DeepSeek models for faster inference