RPi 5 + AI HAT+ 2

What can it run?

Pick a job or a model size. The bar compares what it needs with this machine's usable memory.

• Model weights
• Context cache
• Runtime + extras (Whisper, embeddings)

How we calculate this

Weights = parameters × bits per weight / 8 (Q4_K_M ≈ 4.85; Q8_0 ≈ 8.5). Cache = context tokens × the reference model's per-token size (layers × KV heads × head size × 2 × 2 bytes). Plus 1.5 GB of runtime. Usable memory = total minus what the system keeps (3–6 GB on shared memory, 0.5 GB on a GPU). Comfortable = fits in 85%. It is a ±20% estimate: measure it on your machine before you decide. How to pick the GGUF file

When does it beat the cloud?

Set your usage. We compare the purchase plus electricity with what you would pay a cloud API.

Cumulative cost by month

Month	Own hardware	Cloud

• Own hardware (purchase + electricity)
• Cloud API

Assumptions: €0.25/kWh (Spain average, editable), USD 1 = €0.92, 3 input tokens per output token, and the machine at maximum draw for every hour it is on (worst case). API prices verified 2026-09-09. Your time and maintenance are not included. Note: the cloud side is a frontier model and here you would run a smaller open one; this compares cost, not quality.