Run LLMs locally on Raspberry Pi 5 for Edge AI

Overview

This Learning Path walks you through deploying an efficient large language model (LLM) locally on the Raspberry Pi 5, powered by an Arm Cortex-A76 CPU. This setup enables you to control your smart home using natural language without relying on cloud services. With rapid advances in generative AI and the power of Arm Cortex-A processors, you can now run advanced language models directly in your home on the Raspberry Pi 5.

You will create a fully local, privacy-first smart home system that leverages the strengths of Arm Cortex-A architecture. The system can achieve 15+ tokens per second inference speeds using optimized models like TinyLlama and Qwen, while maintaining the energy efficiency that makes Arm processors well suited for always-on applications.

Why Arm Cortex-A76 makes Raspberry Pi 5 ideal for Edge AI

The Raspberry Pi 5’s Arm Cortex-A76 processor can manage high-performance computing tasks like AI inference. Key architectural features include:

• Superscalar architecture: Executes multiple instructions in parallel, improving throughput for compute-heavy tasks
• 128-bit NEON SIMD support: Accelerates matrix and vector operations, common in the inner loops of language model inference
• Multi-level cache hierarchy: Reduces memory latency and improves data access efficiency during runtime
• Thermal efficiency: Enables sustained performance without active cooling, making it ideal for compact or always-on smart home setups

These characteristics make the Raspberry Pi 5 well suited for workloads like smart home assistants, where responsiveness, efficiency, and local processing are important. Running LLMs locally on Arm-based devices brings several practical benefits. Privacy is preserved, since conversations and routines never leave the device. With optimized inference, the system can offer responsiveness under 100 ms, even on resource-constrained hardware. It remains fully functional in offline scenarios, continuing to operate when internet access is unavailable. Developers also gain flexibility to customize models and automations. Additionally, software updates and an active ecosystem continue to improve performance over time.

Leverage the Arm ecosystem for Raspberry Pi Edge AI

For the stack in this setup, Raspberry Pi 5 benefits from the extensive developer ecosystem:

• Optimized compilers including GCC and Clang with Arm-specific enhancements
• Native libraries such as gpiozero and lgpio are optimized for Raspberry Pi
• Community support from open-source projects where developers contribute Arm-optimized code
• Backward compatibility in Arm architecture reduces friction when updating kernels or deploying across platforms
• The same architecture powers smartphones, embedded controllers, edge devices, and cloud infrastructure—enabling consistent development practices across domains

Performance benchmarks on Raspberry Pi 5

The table below shows inference performance for several quantized models running on a Raspberry Pi 5. Measurements reflect single-threaded CPU inference with typical prompt lengths and temperature settings suitable for command-based interaction.

LLM benchmark insights on Raspberry Pi 5

• Qwen 0.5B and TinyLlama 1.1B deliver fast token generation and low average latency, making them suitable for real-time interactions such as voice-controlled smart home commands
• DeepSeek-Coder 1.3B and Gemma 2B trade some speed for improved language understanding, which can be useful for complex tasks or context-aware prompts
• DeepSeek-R1 7B offers advanced reasoning capabilities with acceptable latency, which may be viable for offline summarization, planning, or low-frequency tasks

Supported Arm-powered devices

This Learning Path focuses on the Raspberry Pi 5, but you can adapt the concepts and code to other Arm-powered devices.

Recommended platforms

Additionally, the platform must meet the following requirements:

• GPIO pins available for hardware control
• Python 3.8 or newer
• Ability to run Ollama

In the next section, you’ll set up the software dependencies needed to start building your privacy-first smart home system on Raspberry Pi 5.