Running DeepSeek LLM Models Locally on Your PC: Hardware Requirements and Deployment Guide
5 Feb 2025
The landscape of large language models (LLMs) is evolving rapidly, and innovations from companies like DeepSeek have made it possible to run these powerful models on your personal computer. In this guide, we explain the hardware requirements for running DeepSeek’s distilled models locally and then walk you through the process of deploying them.
Part 1: Hardware Requirements
DeepSeek has released a range of “distilled” models—from compact 1.5 billion (1.5B) parameter versions to larger variants with tens of billions of parameters. Naturally, the hardware requirements scale with model size:
Small Models (e.g., 1.5B Parameters)
- System Resources: A modern CPU with at least 8 GB of RAM can run these models without a dedicated GPU. However, for faster inference, an NVIDIA RTX 3060 (12 GB VRAM) is recommended.
- Ideal Use Cases: Basic chat interactions, simple coding help, and low-intensity reasoning tasks.
Mid-Range Models (e.g., 7B–8B Parameters)
- System Resources: While CPU-only setups are possible, performance significantly improves with a GPU that has 8–12 GB of VRAM (such as the RTX 3060 or RTX 3080).
- Ideal Use Cases: Enhanced reasoning and code generation tasks without the need for enterprise-level hardware.
Mid-Range to Higher-End Models (e.g., 14B–32B Parameters)
- System Resources: Models in this range often require GPUs with 12–24 GB of VRAM. For example, a 14B model works best with 12–16 GB, while a 32B model may need around 24 GB.
- Ideal Use Cases: Applications demanding advanced reasoning and extensive code assistance, suitable for research or professional development.
Large Models (e.g., 70B and Beyond)
- System Resources: High-end models demand significant resources. Running a 70B model might require 80–180 GB of VRAM, typically through multi-GPU setups (for example, clusters of NVIDIA RTX 4090s or NVIDIA A100s).
- Ideal Use Cases: Enterprise-level applications and research environments that require high accuracy and fast processing speeds.
Recommended GPUs:
• For small models: NVIDIA RTX 3060 (12 GB VRAM)
• For mid-range models: NVIDIA RTX 3080/4070 (8–12 GB VRAM)
• For high-end models: NVIDIA RTX 4090 (24 GB VRAM)
• For enterprise setups: NVIDIA A100 (80 GB VRAM) or multi-GPU configurations
Part 2: How to Run DeepSeek LLM Models Locally
With the right hardware in place, deploying DeepSeek’s models locally is made easier by several modern tools. Below is a simple guide using the popular runtime, Ollama, as well as options for a graphical interface.
Step 1: Installing a Local Runtime with Ollama
Ollama is an open-source tool that simplifies downloading and running LLM models locally. Follow these steps:
- Download and Install Ollama: Visit the Ollama website and install the version for your operating system.
- Pull a DeepSeek Model: Open your terminal and run a command such as:
ollama run deepseek-r1:1.5b
or for a mid-range version:ollama run deepseek-r1:7b
Step 2: Running the Model via CLI or GUI
You have two options to interact with your model:
- Command-Line Interface (CLI): Run the model directly in your terminal for quick tests:
ollama run deepseek-r1:7b - Graphical User Interface (GUI): Use tools such as Open WebUI or Chatbox for a more user-friendly experience. For example, using Docker with Open WebUI:
docker run -d --name open-webui -p 3000:3000 -v open-webui-data:/app/data ghcr.io/open-webui/open-webui:main
Then, access http://localhost:3000 in your browser.
Step 3: Fine-Tuning and Optimization (Optional)
Advanced users may adjust parameters like CPU threads, GPU layers, and batch size to optimize performance. Check the runtime documentation for flags such as --threads and --ngl to fine-tune your setup.
Conclusion
Running DeepSeek LLM models locally offers powerful benefits—complete data privacy, zero recurring API costs, and full control over your AI applications. By matching your hardware to the model’s size and using tools like Ollama, Open WebUI, or Chatbox, you can easily deploy these advanced models on your PC.
Whether you’re using a modest setup for smaller models or a multi-GPU system for enterprise-grade versions, the key is understanding your hardware needs and leveraging the right deployment tools. For instance, if you need faster performance on small to mid-range models, consider upgrading to an RTX 3060 or an RTX 3080. And when it comes to high-end processing, a cluster of RTX 4090s can make all the difference.
Happy deploying!
Sources: workos.com, huggingface.co, apxml.com, martech.org, datacamp.com, geeky-gadgets.com