This Raspberry Pi 5 AI Hack Might Replace Your Expensive Desktop Setup

Can a Raspberry Pi 5 really power local AI inference with the help of a dedicated GPU?

That's the question one YouTuber set out to answer in a fascinating hands-on experiment combining a Pi 5, an AMD GPU, and a whole lot of Linux wizardry. The goal? Run large language models like Mistral-7B entirely offline—without relying on cloud infrastructure or bulky desktop rigs.

Why Go Local for AI?

As AI becomes more integrated into our daily workflows, privacy, cost, and autonomy are taking centre stage. Running LLMs locally means:

Zero cloud fees
Full control over data
Offline functionality
Customisable performance

But let's be honest—most local LLM solutions require serious hardware. The Raspberry Pi 5 challenges that assumption, offering just enough horsepower to run a minimal Linux distro and interface with a GPU.

The Hardware Setup

Here's what was used in the demo:

Raspberry Pi 5 (8GB RAM)
Active cooling kit
PCIe x1 to x16 riser board
AMD Radeon GPU (RDNA2-based)
Powered PCIe riser with external PSU

The Raspberry Pi 5 supports PCIe 2.0 x1 natively. With an adapter, this opens the door to dedicated GPUs—though you're limited in bandwidth. Still, it's enough for quantised models that don't need massive VRAM or bus speeds.

Software & Model Setup

Software stack included:

Ubuntu 22.04 LTS (64-bit for ARM)
ROCm for AMD GPU drivers
Ollama to manage LLMs like Mistral and LLaMA

Ollama makes it surprisingly simple to launch a quantised Mistral model with one command. On the Pi 5 + GPU combo, response times were within 2–3 seconds per token—not lightning-fast, but usable.

Power Consumption Benchmarks

This setup is stunningly efficient in terms of power:

Idle (Pi + GPU): ~15W
Inference load: ~60–70W

Compared to a full desktop setup (often consuming 300–600W), this is a fraction of the cost and energy. Ideal for always-on setups, home labs, and privacy-first projects.

The Catch: What Doesn't Work

Before you rush to buy parts, there are some hard truths:

Driver support: AMD ROCm isn’t perfect on ARM. Some cards work, others won’t.
PCIe bottleneck: You're limited by x1 bandwidth. Complex models won’t load efficiently.
Thermals: Active cooling is essential. This setup runs hot.
Compatibility: NVIDIA GPUs won’t work directly with the Pi due to driver issues on ARM.

"It works great—for the right person. But it’s not plug-and-play. Expect to tinker, troubleshoot, and learn Linux CLI."

Better Alternatives?

If you want to run LLMs locally but don’t want to tinker, here are a few alternatives:

Jetson Orin Nano: More expensive, but GPU-ready and better support
Used mini PCs: Older Intel NUCs or Ryzen boxes can run LLMs with low power
MacBook M1/M2: Native Ollama support with surprising performance

So… Who Is This For?

This hack isn’t for everyone. But if you:

Have a spare AMD GPU
Love low-level hardware projects
Want full control over your AI stack
Enjoy the challenge of building something most people wouldn’t dare attempt

—then this might be the most fun and rewarding weekend project of your year.

Final Thoughts

The Raspberry Pi 5 is no longer just a hobbyist's toy. Paired with the right GPU and a good understanding of Linux, it can become a shockingly capable AI device that fits in your pocket and sips power like a mobile phone.

It’s not perfect, but it’s proof that the future of AI doesn’t have to belong to the cloud giants. Sometimes, it belongs to the curious tinkerer with a Pi and a dream.

🎥 Watch the full setup and demo: Raspberry Pi 5 + GPU Running AI