Running AI agents locally means the inference happens on hardware you own and control, no API costs, no rate limits, no data leaving your network. Ranked by AI inference performance, RAM, and total cost to build a working agent server.
Small language models (1B–7B parameters) run on 4–8GB RAM boards with acceptable speed. 7B models like Mistral, Llama 3.1 8B, and Phi-3 run at 5–15 tokens/second on Raspberry Pi 5, usable for background tasks and automation. The NVIDIA Jetson's dedicated Neural Processing Unit at 40 TOPS can run larger models faster and handles computer vision simultaneously. Ollama is the tool that makes model management simple on all of these boards. Install Ollama, pull a model, run it locally.
| Model | AI Inference | RAM | NVMe Storage | Price |
|---|---|---|---|---|
| NVIDIA Jetson Orin Nano Super | 40 TOPS NPU + GPU | 8GB LPDDR5 | M.2 built-in | ~$249 |
| Orange Pi 5 Plus 8GB | 6 TOPS NPU | 8GB LPDDR4X | M.2 built-in | ~$140 |
| Raspberry Pi 5 (8GB) | CPU only | 8GB LPDDR4X | PCIe HAT | ~$170 |
| Raspberry Pi 5 (4GB) | CPU only | 4GB LPDDR4X | PCIe HAT | ~$85 |
40 TOPS of dedicated AI inference hardware is 10–15x more capable than running inference on CPU alone. The Jetson runs LLMs, computer vision, and sensor processing simultaneously, the real use case for an AI agent that needs to see, hear, and respond at the same time. It's the only board here with a dedicated GPU designed for AI workloads. The correct pick if you're building something that needs actual real-time AI performance.
The RK3588 is the best ARM chip for the price in 2026, faster than Raspberry Pi 5 on CPU tasks and includes a 6 TOPS NPU for AI acceleration. Dual 2.5GbE makes it exceptional as a home server running multiple AI agent services. Orange Pi software support is thinner than Raspberry Pi's community, but Armbian and official Ubuntu images work well. Best dollar-for-performance ratio for a capable local AI server.
The Raspberry Pi 5 has no dedicated AI inference hardware, but it has the largest community, the most tutorials, and the most compatible AI tooling (Ollama, LangChain, Home Assistant). If your goal is to run a 3B–7B model locally and automate home or business tasks, the Pi 5 8GB does it. Slower than the Jetson for inference, but the ecosystem and documentation support more than compensates for most learning-stage users. Add a NVMe HAT for storage.
4GB of RAM limits you to smaller models (Phi-3 mini, TinyLlama, Gemma 2B) but those are more capable than they sound for specific tasks, email classification, home automation triggers, summarization, simple chat. At $85 the Pi 5 4GB is the correct entry point for learning local AI without the commitment of spending $250 on a Jetson. When you outgrow 4GB, you understand exactly what you need, and the upgrade path is clear.