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From a $150 Raspberry Pi to a $4,000 Mac Studio with 512GB of unified memory. Every device that lets you run models locally, host OpenClaw, and stop paying per token.
Mac Mini M4
Daily Driver| DEVICE | PRICE | RAM | AI TOPS | MEM BW | POWER | BEST FOR | OPENCLAW |
|---|---|---|---|---|---|---|---|
Raspberry Pi 5 + AI HAT+ 2 | ~$150 | 8GB LPDDR4X | 40 TOPS | 16 GB/s | 5–27W | Qwen2 1.5B | ✓ |
Jetson Orin Nano Super Developer Kit | $249 | 8GB LPDDR5 | 67 TOPS | 102 GB/s | 7–25W | Phi-3 Mini 3.8B | ✓ |
Jetson AGX Orin 64GB | ~$999 | 64GB LPDDR5 | 275 TOPS | 204 GB/s | 15–60W | Phi-3 Medium 14B | ~ |
Beelink SER9 Pro Ryzen AI 9 HX 370 | ~$499 | 32GB LPDDR5X | 80 TOPS | 89 GB/s | 35–65W | Llama 3 8B | ✓ |
Mac Mini M4 | $599 | 16–32GB Unified | 38 TOPS | 120 GB/s | ~30W | Gemma 2 9B | ✓ |
Mac Mini M4 Pro | $1,399 | 24–64GB Unified | 55 TOPS | 273 GB/s | ~30–60W | Llama 3 70B (Q4) | ✓ |
Mac Studio M4 Max | $1,999 | 36–128GB Unified | ~500 TOPS | 410 GB/s | ~100–140W | Qwen 72B (Q5) | ✓ |
Mac Studio M3 Ultra | $3,999 | 96–512GB Unified | ~800 TOPS | 819 GB/s | ~180–300W | Llama 3 405B (Q4) | ✓ |
| MODEL | Raspberry Pi 5 + AI HAT+ 2 | Jetson Orin Nano Super Developer Kit | Jetson AGX Orin 64GB | Beelink SER9 Pro Ryzen AI 9 HX 370 | Mini M4 | Mini M4 Pro | Studio M4 Max | Studio M3 Ultra |
|---|---|---|---|---|---|---|---|---|
TinyLlama 1.1B 1.1B · Q4 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Phi-3 Mini 3.8B 3.8B · Q4 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Llama 3 8B 8B · Q4 | — | — | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Mistral 7B 7B · Q4 | — | — | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Gemma 3 27B 27B · Q4 | — | — | — | — | — | ✓ | ✓ | ✓ |
Qwen 2.5 32B 32B · Q4 | — | — | — | — | — | ✓ | ✓ | ✓ |
Llama 3 70B 70B · Q4 | — | — | — | — | — | — | ✓ | ✓ |
DeepSeek V3 / R1 671B (MoE) · Q4 | — | — | — | — | — | — | — | ✓ |
Llama 3 405B 405B · Q4 | — | — | — | — | — | — | — | ✓ |
You want to learn, experiment, run small models at home, automate things. The Pi with Hailo NPU runs Qwen 1.5B and small vision models. Perfect OpenClaw node on your network.
Best everyday AI box at sub-$600. Both run 8B models smoothly. Mac Mini wins on power draw and Ollama ecosystem. Beelink wins if you need Windows or a CUDA-adjacent pipeline.
273 GB/s memory bandwidth is the unlock. You can run Qwen 32B, Mistral 22B, and squeeze 70B quantised. This is the machine that replaces most cloud API spend for devs.
Your personal inference server. Runs 70B models fast enough to not hate your life. Host OpenClaw, run voice pipelines, do fine-tuning, serve APIs locally. This is the home lab.
You want Llama 3 405B at home. The 512GB BTO config is the only consumer machine that fits it unquantised. 819 GB/s bandwidth. Your private frontier node. No subscription, no rate limits.
You're building robots, autonomous systems, or embedded AI. CUDA-native, TensorRT-ready, JetPack ecosystem. Not for everyday desktop use — built for inference pipelines that deploy to the edge.
It Is All About Memory Bandwidth
Running a large language model is a memory-bandwidth problem, not a compute problem. The bottleneck is how fast you can stream model weights from RAM into the compute cores. A Mac Mini M4 Pro has 273 GB/s of unified memory bandwidth. A typical Windows mini PC with DDR5 has 50–90 GB/s. The Mac runs 70B models. The Windows PC chokes on 13B.