How to Run DeepSeek Locally on Your Computer windows

Running DeepSeek LLM locally on a Windows machine requires setting up the model using compatible frameworks like Ollama, LM Studio, or GPTQ-based tools. Here’s how you can do it, along with the hardware requirements.

1. Hardware Requirements

DeepSeek models come in different sizes. Here’s a breakdown of the recommended hardware:

Model Size	RAM (Minimum)	VRAM (GPU)	Storage	CPU Requirement
DeepSeek 7B	16GB	6GB (RTX 3060 or better)	15GB	6-core or better
DeepSeek 67B	128GB	48GB (RTX 4090 or A100)	120GB	High-end CPU (Threadripper/Xeon)

2. Setting Up DeepSeek Locally on Windows

Method 1: Using LM Studio (Easiest)

Download LM Studio
👉 Get it here
Install and Open LM Studio
Download DeepSeek Model (GGUF format)

Search for DeepSeek 7B GGUF (smaller size, runs on CPU/GPU)

Run the Model Locally

Load it in LM Studio and interact via the interface.

Method 2: Using Ollama (For CLI Users)

Install Ollama
👉 Download here
Open Command Prompt and Install DeepSeek

   ollama pull deepseek-ai/deepseek-coder

Run the Model

   ollama run deepseek-coder

Method 3: Using GPTQ for GPU Acceleration

Install Python & CUDA Toolkit (For NVIDIA GPUs)
Set Up text-generation-webui

   git clone https://github.com/oobabooga/text-generation-webui.git
   cd text-generation-webui
   pip install -r requirements.txt

Download a GPTQ-optimized DeepSeek Model (from Hugging Face)
Run the Web UI

   python server.py

Which Method Should You Choose?

For beginners: Use LM Studio (easy GUI setup)
For CLI users: Use Ollama
For GPU acceleration: Use GPTQ + text-generation-webui

Was this article helpful?

YesNo

General

DeepSeek R1 RAM Requirements

ByTeam February 7, 2025February 7, 2025

IntroductionDeepSeek R1 is an advanced AI model designed for various deep learning tasks, including natural language processing, stock market prediction, and data analysis. Deploying DeepSeek R1 efficiently requires an understanding of its hardware requirements, particularly RAM, CPU, and GPU specifications. This guide provides a comprehensive overview of the necessary hardware configurations for inference and training….

General