What is DeepSeek?
DeepSeek is an open-source large language model (LLM) designed for natural language processing (NLP) tasks, similar to OpenAI’s ChatGPT. Developed by DeepSeek AI, it aims to provide high-performance AI capabilities with transparency and flexibility, allowing developers to run models locally.
DeepSeek models come in various parameter sizes, making them suitable for different levels of computational power. Below are the DeepSeek Parameters and a small brief about them
1. DeepSeek-Coder 1.5B
- Parameters: 1.5B
- Best for: Code completion, lightweight AI applications
- Hardware Requirements: 4GB VRAM, 8GB RAM
- Use Case: Runs efficiently on most consumer laptops or low-end GPUs (GTX 1650, RTX 3050).
2. DeepSeek-Coder 7B
- Parameters: 7B
- Best for: General programming tasks, chatbot interactions
- Hardware Requirements: 8GB VRAM, 16GB RAM
- Use Case: Good for developers looking for an open-source alternative to ChatGPT with local execution. Runs on mid-range GPUs (RTX 3060, RTX 3070).
3. DeepSeek-Coder 8B
- Parameters: 8B
- Best for: AI-assisted programming, improved reasoning capabilities
- Hardware Requirements: 10GB VRAM, 16GB RAM
- Use Case: Slightly more capable than the 7B model, requires a powerful consumer GPU (RTX 3080+).
4. DeepSeek 14B
- Parameters: 14B
- Best for: Advanced NLP tasks, enhanced conversational abilities
- Hardware Requirements: 24GB VRAM, 32GB RAM
- Use Case: Ideal for high-end consumer GPUs (RTX 4090) and deep AI research projects.
5. DeepSeek 32B
- Parameters: 32B
- Best for: More complex AI-driven applications, code generation, research
- Hardware Requirements: 40GB VRAM, 64GB RAM
- Use Case: Runs on A100 40GB or similar high-end hardware, suited for AI professionals and researchers.
6. DeepSeek 70B
- Parameters: 70B
- Best for: Enterprise-level AI solutions, advanced AI development
- Hardware Requirements: 80GB VRAM, 128GB RAM
- Use Case: Runs on A100 80GB or multiple GPUs; best for large-scale AI training and enterprise use.
7. DeepSeek 671B
- Parameters: 671B
- Best for: Cutting-edge AI research, multimodal applications
- Hardware Requirements: Multiple A100s/H100s, 512GB+ RAM
- Use Case: Supercomputer-level AI applications, requires cloud-based or specialized hardware for deployment.
What is Ollama?
Ollama is a lightweight, open-source tool designed to run large language models (LLMs) locally on personal computers and servers. It simplifies model deployment by handling model downloading, execution, and optimization without requiring complex setups.
Key Features of Ollama
- Runs LLMs locally without internet dependency.
- Supports multiple models, including DeepSeek, LLaMA, Mistral, and more.
- Optimized for efficiency, leveraging GPU acceleration when available.
- Simple installation with minimal setup required.
- Cross-platform support for macOS, Linux, and Windows (via WSL).
Why Use Ollama for DeepSeek?
- Easy to install and run DeepSeek models locally.
- Supports consumer GPUs (RTX 3060+) and high-end AI hardware
- No cloud dependency, ensuring full privacy & control
Now let us look at how to run the DeepSeek model locally on your computer:
Benefits of Running DeepSeek Locally:
Running DeepSeek locally provides several advantages, especially for developers, researchers, and enterprises looking for privacy, customization, and cost efficiency.
1. Privacy & Data Security
- No sensitive data is sent to external servers.
- Ensures full control over user inputs and model interactions.
- Ideal for businesses handling confidential or proprietary information.
2. No API Costs & Subscription Fees
- Running DeepSeek locally eliminates recurring API costs from cloud providers.
- Cost-effective for enterprises using AI at scale.
3. Full Customization & Fine-Tuning
- Modify, train or fine-tune the model according to specific requirements.
- Create domain-specific AI models for programming, healthcare, finance or other industries.
4. Offline Accessibility
- No internet connection is required after setup.
- Useful for edge computing, remote locations, and air-gapped environments.
5. Low Latency & Faster Response Times
- Eliminates network delays associated with cloud-based models.
- Ideal for real-time applications like chatbots, code generation, and automation.
6. Scalability & Hardware Flexibility
- Choose a model size (from 1.5B to 70B) based on hardware capabilities.
- Compatible with consumer GPUs (RTX 3060+) or high-end AI accelerators (A100, H100).
7. Freedom from Cloud Restrictions
- No dependency on external cloud providers like OpenAI, Google, or AWS.
- Avoids API limitations, throttling, and data retention policies.
Who Should Run DeepSeek Locally?
- Developers working on AI-assisted coding.
- Enterprises handling sensitive data.
- Researchers needing model customization.
- Organizations looking to cut cloud costs.
Step by step guide to easily setup DeepSeek using Ollama, Docker and OpenWebUi
1: Installing ollama
- Go to https://ollama.com/
- Download Ollama for windows
- Install Ollama
2: Downloading model
- Go to https://ollama.com/
- Switch to the models tab
- Search for the deepseek model in the list
- Download the deepseek-r1 model. When u click the deepseek-r1 u will find the other models of deepseek listed.
- Select the deepseek model which u need to run.
- Copy the command
- Run the command on PowerShell and wait for complete pull.
- After the pull and now we can run the model on our PowerShell itself without any UI just as command-line interface or we can integrate it with UI.
- To integrate it with UI we will need docker and OpenWebUI.
3: Install docker
- Go to Docker.com
- Install the docker to your system
- Open docker
4: Install OpenWebUI for the UI interface
- Go to docs.openwebui.com
- Search for Ollama with docker and download the code and run it in PowerShell
- Then you will find a container has been created in docker
- Click the container and open the localhost you can find the WebUI running on localhost with the DeepSeek model.
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