The Edge AI Revolution

The Raspberry Pi has always been the champion of tiny computing. But Large Language Models (LLMs) like Llama-3 require massive GPUs and gigabytes of VRAM. Can a Pi 5 actually handle them?

The Experiment: Llama-3-8B on 8GB Pi 5

Using llama.cpp and Ollama, we tried to run an 8-billion parameter model.

The Results:

• Loading Time: ~15 seconds.
• Speed: 1.5 - 2.0 tokens per second. (Roughly the speed of a human reading slowly).
• RAM Usage: ~5GB (Leaving plenty for the OS).

How We Made It Work

The secret is Quantization. By shrinking the model weights from 16-bit to 4-bit (GGUF format), we Reduced the memory footprint by 4x without losing significant “intelligence.”

The Verdict

While you won’t be building the next Jarvis on a Pi, it is perfectly capable of running local, private automation agents for smart homes or simple Q&A.

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References & Further Reading

• Raspberry Pi Foundation: AI on the Pi 5
• Llama.cpp Repository: CPU Inference
• Edge AI Blog: Optimizing for ARM