Last updated May 2026.
This guide covers running local LLMs on the Raspberry Pi 5. These configurations are sourced from real developer setups in the community to give you the exact insights that work right now.
The Raspberry Pi 5 has introduced enough compute power to make local LLM inference a practical reality for embedded systems developers. Community reports show that running quantized 1B and 3B parameter models at usable speeds is now achievable. This guide covers the specific models and configurations that the community is using to run AI assistants directly on the Pi 5.
The primary challenge is RAM. The Pi 5 maxes out at 8GB, which severely limits model size. Community developers recommend using the GGUF format via llama.cpp, as it provides the best CPU-only performance on ARM64 hardware. We analyze the specific compilation flags used by builders to maximize tokens-per-second on the Pi 5’s quad-core Cortex-A76 CPU.
What the community found
For edge AI applications, the Pi 5 with a local LLM provides a fully offline, privacy-first assistant. The most common community use case is a local home automation controller that can interpret natural language commands without internet access. We cover the specific system prompt setups and model pairings that make this reliable.
Frequently Asked Questions
Q: Which LLM model works best on the Raspberry Pi 5?
A: Community reports favor Phi-3 Mini (3.8B) and Llama 3.2 1B as the top performers. Both are small enough to run within 4GB of RAM and provide surprisingly coherent responses for their size.
Q: How fast is llama.cpp on the Raspberry Pi 5?
A: On the Pi 5 with 8GB RAM, developers report 5 to 12 tokens-per-second for a 3B GGUF Q4 model. While slower than a desktop GPU, this is sufficient for interactive chat and automation tasks.
Q: Can I connect the Raspberry Pi 5 to a GPU for faster LLM inference?
A: Yes. The Pi 5’s PCIe interface supports a HAT+ M.2 adapter, enabling connection to small NVIDIA or AMD GPUs. Community builders have used this to run larger models with significantly improved tokens-per-second.
Q: Is the Raspberry Pi 5 better than an older Pi 4 for LLM inference?
A: Significantly better. The Pi 5’s Cortex-A76 cores are roughly 2x to 3x faster than the Pi 4’s Cortex-A72, making the Pi 5 the minimum recommended hardware for a practical local LLM experience.
One response to “AI Agent Memory Systems: Building Persistent Context for Your Bot”
[…] The write-it-down rule that matters most: if you want the agent to remember something, it must write it to a file. There is no hidden mental note storage inside the model. Treat the filesystem as the brain. We broke this down fully in AI Agent Memory Systems: Building Persistent Context for Your Bot. […]