Deepseek on Raspberry Pi: Unlocking AI Potential on a Compact Edge Device

Feb 25th, 2025

Deepseek is an advanced AI-driven framework or software designed for tasks such as object detection, image recognition, natural language processing (NLP), or other machine learning applications. It leverages deep learning models to analyze and interpret complex data, making it a powerful tool for various industries and research fields. Deepseek is often optimized for efficiency, enabling it to run on resource-constrained devices like the Raspberry Pi.

Overview of Deepseek AI models

Deepseek AI models are a collection of pre-trained or customizable deep learning models designed to address a variety of tasks across different domains. These models are optimized for efficiency, accuracy, and scalability, making them suitable for deployment on both high-performance systems and resource-constrained devices like the Raspberry Pi.
    ✔ DeepSeek V3: Can be considered a powerful all-rounder for scalable language processing. It is highly efficient and is characterized above all by its ability to explain concepts in an understandable and simple way.
    ✔ DeepSeek R1: Is a specialist in reasoning for academic problems. His greatest strengths lie in his excellent problem-solving skills, mathematical thinking, and logical analysis. He is especially suited for educational resources, research purposes, and AI-assisted reasoning.

Potential use cases for running Deepseek on Raspberry Pi

• Surveillance Systems:
      o Real-time monitoring and tracking of objects or people.
      o Intrusion detection and alert systems.
• Robotics:
      o Object recognition for autonomous navigation.
      o Pick-and-place operations in industrial robots.
• Smart Home Devices:
      o Gesture recognition for smart appliances.
      o Pet or child monitoring systems.
 • Retail and Inventory Management:
      o Product recognition and tracking on shelves.
      o Automated checkout systems.
• Education and Prototyping:
      o Teaching AI and computer vision concepts.
      o Building proof-of-concept projects.

Challenges and limitations

CPU Performance: Raspberry Pi models, even the latest ones like the Raspberry Pi 4 or 5, have relatively low computational power compared to modern desktop CPUs.
Deep learning models, especially large ones, require significant processing power for both training and inference.

Lack of GPU Acceleration: Most Raspberry Pi models do not have a dedicated GPU, and the integrated GPU is not powerful enough to accelerate deep learning tasks effectively. This limits the ability to run complex models efficiently.

RAM Limitations: Raspberry Pi devices typically have limited RAM (1GB to 16GB in the latest models). Deep learning models, particularly those with large datasets or complex architectures, can quickly exhaust available memory, leading to performance bottlenecks or crashes.

While it is possible to run deep learning tasks on a Raspberry Pi, it is generally limited to lightweight models and inference tasks. For more complex or resource-intensive applications, it is often necessary to use more powerful hardware or offload computations to external servers or cloud-based services. Optimizing models and leveraging techniques like model pruning, quantization, and efficient coding practices can help mitigate some of these challenges, but the inherent limitations of the Raspberry Pi hardware will always be a factor.

Prerequisites

Hardware requirements

Installing Deepseek on Raspberry Pi 5 8GB

Running a Lightweight AI Model Locally

Ollama is an open-source framework that allows users to run large language models (LLMs) locally on their own machines. It simplifies the process of downloading, running, and interacting with AI models without needing cloud-based services.

Ollama provides an efficient LLM runtime, supporting quantized models to reduce memory usage and improve performance on lower-end hardware.

1. Install Ollama
Try running the official Ollama install script:
curl -fsSL https://ollama.com/install.sh | sh

2. Once Ollama is installed, download a compatible DeepSeek model:
 ollama pull deepseek-r1:1.5b
This command fetches the 1.5 billion parameter version of DeepSeek-R1, which requires approximately 1.8GB of storage.

3. Run the Model
After downloading, you can start an interactive session:

ollama run deepseek-r1:1.5b

 ONNX and TensorFlow Lite: Running DeepSeek on Raspberry Pi

Since DeepSeek models are large and computationally expensive, frameworks like ONNX Runtime and TensorFlow Lite (TFLite) help optimize inference for low-power devices like Raspberry Pi.

ONNX (Open Neural Network Exchange) is an open-source format for AI models. It allows models trained in PyTorch, TensorFlow, and other frameworks to run efficiently across different platforms.

ONNX is the best option for running DeepSeek on Raspberry Pi due to its lightweight execution and ARM support.

TensorFlow Lite (TFLite) is a lightweight version of TensorFlow optimized for mobile and embedded devices like Raspberry Pi.
It uses quantization to reduce model size and improves execution speed on low-power hardware.

TFLite is ideal for edge AI applications but requires models to be converted into TFLite format before running.

Example: Using ONNX to Run DeepSeek on Raspberry Pi

1. Install ONNX Runtime
pip install onnxruntime

2. Convert DeepSeek Model to ONNX
If you have a PyTorch-based DeepSeek model, convert it into ONNX:

import torch
import torch.onnx #
Load DeepSeek model
model = torch.load("deepseek_model.pth")
 model.eval()

# Dummy input tensor (change shape based on model input size)
 dummy_input = torch.randn(1, 3, 224, 224)
 # Export the model to ONNX
 torch.onnx.export(model, dummy_input, "deepseek_model.onnx",
                   export_params=True, opset_version=11, do_constant_folding=True)

3. Step 3: Run DeepSeek ONNX
 Model on Raspberry Pi
Once converted, you can run inference using ONNX Runtime:
 import onnxruntime as ort
 import numpy as np
# Load ONNX model
session = ort.InferenceSession("deepseek_model.onnx")
# Prepare input data
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)
input_name = session.get_inputs()[0].name
# Run inference
output = session.run(None, {input_name: input_data})
print("Model Output:", output)

Performance Optimization

Using lightweight versions of Deepseek models
DeepSeek-R1 offers several distilled models with reduced parameters, making them more suitable for devices with limited resources.
Available Lightweight Models:
• DeepSeek-R1:1.5B: Approximately 1.5 billion parameters.
• DeepSeek-R1:7B: Approximately 7 billion parameters.
• DeepSeek-R1:8B: Approximately 8 billion parameters.
For the Raspberry Pi 5, the 1.5B model is recommended due to its lower resource demands.
Optimize Memory Usage (Enable Swap Memory)
Increase Swap to 2GB. This prevents Out-of-Memory (OOM) errors when running DeepSeek.
sudo dphys-swapfile swapoff
sudo sed -i 's/CONF_SWAPSIZE=.*/CONF_SWAPSIZE=2048/' /etc/dphys-swapfile
sudo dphys-swapfile setup
sudo dphys-swapfile swapon

Conclusion: Best Way to Run DeepSeek on Raspberry Pi

✔  Full DeepSeek models (V3, R1) are too large to run directly.
✔ Use quantized 1.5B models with ONNX or TensorFlow Lite.
✔ Use external AI accelerators (Google Coral, Intel NCS2) for real-time inference.
✔  For best performance, use DeepSeek API instead of running locally.

Conclusion

Deepseek on Raspberry Pi opens up exciting possibilities for deploying AI models on compact, low-power devices. While the Raspberry Pi has hardware limitations, optimizations such as quantization, ONNX Runtime, and TensorFlow Lite allow for efficient inference. Additionally, external AI accelerators like Google Coral TPU and Intel NCS2 can further enhance performance. For those looking to run full-scale Deepseek models, leveraging cloud-based APIs remains a practical alternative. Whether for AI-powered robotics, smart home automation, or educational prototyping, Deepseek on Raspberry Pi provides a cost-effective gateway into the world of edge AI. 🚀