How to Run DeepSeek Locally on Android: Complete Guide

January 19, 2026

In the rapidly evolving world of artificial intelligence, DeepSeek has emerged as a powerful open-source competitor to models like ChatGPT and Claude. But what if you want to use it without an internet connection?

This guide will educate you on running the DeepSeek-R1 and DeepSeek-Coder models directly on your smartphone. We will cover prerequisites, a detailed step-by-step installation guide using Termux, and alternative user-friendly methods.

Why Run DeepSeek Locally?

Before we dive into the technical steps, it is important to understand the value of running AI locally:

Privacy: Your data never leaves your device. No cloud servers means no risk of data leaks.
Offline Access: Use DeepSeek anywhere, from airplanes to remote cabins, without needing Wi-Fi or 5G.
No API Costs: Running the model on your hardware is free, unlike paying for token usage on cloud APIs.

Prerequisites: Can Your Phone Handle It?

Running Large Language Models (LLMs) locally is resource-intensive. To run DeepSeek effectively, your Android device should meet these minimum specifications:

RAM: 8GB recommended (4GB can run tiny 1.5B parameter models, but expect slowness). 12GB+ is ideal for 7B models.
Processor: Snapdragon 8 Gen 2, Dimensity 9000, or newer high-end chips are preferred for decent inference speeds.
Storage: At least 10GB of free space (for the Linux environment and model weights).
Operating System: Android 10 or higher.

Method 1: The Power User Route (Termux + Ollama)

This is the most flexible and robust method. We will use Termux, a powerful terminal emulator, to create a Linux environment and Ollama to manage and run the DeepSeek models.

Step 1: Install Termux

Do not use the Google Play Store version of Termux as it is outdated. Instead, download it from F-Droid, the trusted catalog for open-source apps.

Go to the Termux F-Droid page.
Download the APK and install it on your device.
Open Termux.

Step 2: Update and Prepare Environment

Enter the following commands one by one in Termux. Press Enter after each line and type y if asked to confirm.

# Update package lists
pkg update && pkg upgrade

# Install essential tools
pkg install git cmake golang

Step 3: Install Ollama

Ollama is the industry standard for running local LLMs easily. While there isn’t a direct Android app yet, we can build it inside Termux.

# Clone the Ollama repository
git clone --depth 1 [https://github.com/ollama/ollama.git](https://github.com/ollama/ollama.git)

# Navigate into the folder
cd ollama

# Build Ollama (This may take 5-10 minutes depending on your phone speed)
go generate ./...
go build .

Step 4: Run the Model

Once built, you can start the server and run the model.

Start the Server: Run the following command to start the Ollama backend:./ollama serve & (Note: The & runs it in the background. Press Enter if the prompt doesn’t reappear immediately).
Run: Now, tell Ollama to download and run the DeepSeek model. For phones, we recommend the 1.5 billion parameter version (deepseek-r1:1.5b) for the best balance of speed and performance../ollama run deepseek-r1:1.5b If you have a high-end phone (12GB+ RAM), you can try the larger version:./ollama run deepseek-r1:7b

Success! You are now chatting with DeepSeek locally on your Android device.

Method 2: The User-Friendly App (PocketPal AI)

If the command line isn’t for you, apps like PocketPal AI provide a graphical interface that handles the backend work.

Download PocketPal AI: Search for it on GitHub or its official website.
Select Model: Open the app and navigate to the model manager.
Search for DeepSeek: Look for “DeepSeek-R1-Distill-Qwen” or similar quantized versions.
Download & Chat: Once downloaded (usually 1-4GB), you can select it and start chatting just like you would with WhatsApp.

Note: These apps often rely on the phone’s NPU (Neural Processing Unit), which can be faster than the CPU-based Termux method on supported devices.

Optimizing Performance for DeepSeek on Android

To ensure DeepSeek runs smoothly:

Kill Background Apps: Free up as much RAM as possible before starting the model.
Use Quantized Models: Always use “quantized” models (e.g., GGUF format). These are compressed versions that sacrifice negligible accuracy for massive speed gains and lower memory usage.
Keep it Cool: LLM inference generates heat. Remove your phone case if you plan on a long session.

By following this guide, you have transformed your smartphone into a private, powerful AI workstation.

How to Run DeepSeek Locally on Android: Complete Guide

Why Run DeepSeek Locally?

Prerequisites: Can Your Phone Handle It?