Many people worry about using cloud-based AI models. Running AI locally solves privacy issues and speeds up responses. You can run DeepSeek locally on your computer without internet dependency.
Here, I will explain everything in simple steps. Follow the instructions to set up DeepSeek AI. Your data stays private, and you get a faster experience.
also read: What is DeepSeek AI? A Game Changer in AI Technology
Why Run DeepSeek Locally on Your Computer?
DeepSeek is a powerful AI model with various features. Running it locally helps in many ways. Here are the main advantages:
- No API limits – Use DeepSeek freely without restrictions.
- No cloud dependency – AI runs fully on your device.
- Better privacy – No risk of data leaks or logging.
- Improved performance – Optimize settings for faster responses.
- ChatGPT-like UI – Use Open WebUI for a smooth interface.
- Customizable – Fine-tune models for different AI tasks.
Running DeepSeek on your computer makes AI more powerful. You can perform text generation, coding, research, and more. DeepSeek R1 also supports a Vision model for image analysis. With the right setup, you can fully utilize these features.
System Requirements for Running DeepSeek Locally
Different DeepSeek models need different hardware setups. Here is a detailed table explaining the requirements:
| Model | RAM Needed | CPU Needed | GPU Required | Best Use Case |
| 1.5B | 8GB+ | Any Modern CPU | No | Basic writing, chat, quick responses |
| 8B | 16GB+ | 4+ Cores (Intel i5/Ryzen 5/M1) | No | General reasoning, longer writing, coding |
| 14B | 32GB+ | 6+ Cores (Intel i7/Ryzen 7/M2) | No | Deeper reasoning, coding, research |
| 32B | 32-64GB+ | 8+ Cores (M3 Pro, Ryzen 9, i9) | Yes | Complex problem-solving, AI-assisted coding |
| 70B | 64GB+ | 12+ Cores (M4 Pro, Threadripper) | Yes | Heavy AI workflows, advanced research |
| 70B Vision | 64GB+ | 12+ Cores (M4 Pro, Threadripper) | Yes | Image analysis, AI-generated visuals |
The higher the model size, the more power it needs. Running smaller models like 8B or 14B works on most computers.
How to Run DeepSeek Locally on Your Computer
Step 1: Install Ollama AI Engine
Ollama is required to run DeepSeek AI models. If your system does not have Python, install it first.
- Open the terminal on your computer.
- Run the command:
/bin/bash -c “$(curl -fsSL https://ollama.com/download)”
- Verify installation with:
ollama –version
If the installation is successful, you are ready for the next step.
Step 2: Download the DeepSeek R1 Model
DeepSeek R1 comes in different sizes. Choose a model based on your computer’s specifications.
- Open the terminal and enter:
ollama pull deepseek-r1:8b # Fast, lightweight
ollama pull deepseek-r1:14b # Balanced performance
ollama pull deepseek-r1:32b # Heavy processing
ollama pull deepseek-r1:70b # Max reasoning, slowest
The larger models require more resources. If you have limited RAM, go for the 8B or 14B model.
Step 3: Run DeepSeek in Terminal
Once the model is downloaded, run it in terminal mode.
- Open the terminal.
- Run:
ollama run deepseek-r1:8b
This will start DeepSeek AI locally on your computer. However, using a terminal interface is not user-friendly.
Step 4: Upgrade to a ChatGPT-Like Interface
To make the experience smoother, install Open WebUI. It provides a friendly chat interface.
Install Docker (Required for Open WebUI)
- Go to Docker’s official website.
- Download Docker for your operating system.
- Install and open Docker on your computer.
Install Open WebUI
- Open the terminal and run:
docker run -d –name open-webui -p 3000:3000 -v open-webui-data:/app/data –pull=always ghcr.io/open-webui/open-webui:main
- Open your browser and go to:
http://localhost:3000
Now, you have a ChatGPT-like interface for DeepSeek AI.
Running DeepSeek Using LM Studio (Alternative Method)
Another way to run DeepSeek locally on your computer is LM Studio. It supports open-source AI models on PC, Mac, and Linux.
Step 1: Install LM Studio
- Download LM Studio from lmstudio.ai.
- Install the software on your computer.
Step 2: Load DeepSeek Model in LM Studio
- Open LM Studio after installation.
- Download the distilled DeepSeek R1 model.
- Load the model manually if required.
- Start using DeepSeek AI locally.
LM Studio provides an easy way to run AI models without technical steps.
Optimizing Performance of DeepSeek AI
To make DeepSeek AI run faster, adjust the following settings:
- Increase Threads – Set thread count to match your CPU cores.
- Use High GPU Layers – Adjust GPU layers for better speed.
- Optimize Batch Size – Increase batch size for faster processing.
- Avoid Multiple Instances – Run one session at full power.
- Monitor Resource Usage – Use system tools to track performance.
For monitoring usage, use these commands:
htop # Shows CPU usage
sudo powermetrics –samplers cpu_power,gpu_power -i 500 # Monitors GPU usage
If the system is slow, reduce the model size or adjust settings.
How to Check if DeepSeek is Running Locally
To verify that DeepSeek is running offline, turn off Wi-Fi and Ethernet. If the AI model still responds, it means it is running locally.
Real-World Performance Tests
Here are the results of running DeepSeek R1 on different setups:
| Model | Time Taken (Python Tetris Code) |
| 1.8B | ~3 minutes 40 seconds |
| 8B | ~6 minutes 53 seconds |
| 14B | ~7 minutes 10 seconds |
| 32B | ~13 minutes 48 seconds |
Performance depends on hardware and system settings.
As We Conclude
Running DeepSeek locally on your computer offers many benefits. It enhances privacy, speeds up responses, and removes API restrictions. With tools like Ollama, Docker, and LM Studio, the setup is simple.
Choose a model that fits your system and optimize settings for better performance. Local AI ensures better control, making it a great option for personal and professional use.
