Generative AI on a Raspberry Pi.
A generative image, video or language model usually assumes a GPU and gigabytes of memory. veizik runs the same models to completion on a Raspberry Pi — on the CPU alone — by carrying the weights in a sub-byte format, executing them with a compile-free native kernel path, and streaming them through a bounded working set. The figures are measured on the real boards.
A GPU’s job, done on four CPU cores.
What makes a generative model fit and finish on a board with no accelerator.
- The same engine, on the CPU. There is no separate “mobile” build. The engine executes the model natively on the board’s Arm cores, vectorised for NEON, with the whole pipeline — text encoder, the diffusion or language model, and the decoder — running on the CPU.
- Sub-byte quantization. veizik’s own compact weight format carries the model in well under a byte per weight, so a model that would not fit in a few hundred megabytes does — and the smaller numbers are faster to move and multiply on a CPU, not just smaller to store.
- No compile step at run time. The GPU-free kernel path is built ahead of time for the exact core, so the first token and the first image come back without waiting on a compiler on the device.
- A bounded working set. The engine streams the model through a fixed amount of memory instead of holding all of it at once, which is what lets a 512 MB Raspberry Pi Zero 2 W finish a job it could not otherwise hold.
- One binary, no framework. The same self-contained engine as everywhere else — no Python, no deep-learning framework, no numeric library — so the dependency list on a Pi is the engine and nothing else.
Measured on the boards, with the renders.
Every figure here is taken on a real Raspberry Pi; the numbers and the generated images and video are on the edge page.
The edge page carries the measured per-model figures (SDXS in about five seconds on a Pi 5 CPU, the full Stable Diffusion 1.5 in seconds with a few-step LCM, a language model on a 512 MB Zero 2 W) and the renders they produced; the gallery shows the output beside the datacenter and Apple Silicon work. The edge engine is arriving soon, with the models released one at a time.
The same engine, all the way down.
- It runs where there is no GPU and no cloud. On-device from the checkpoint, so the data never leaves the board and there is no account, no network and no per-image cost.
- It fits the hardware you have. From an 8 GB Raspberry Pi 5 down to a 512 MB Zero 2 W — the memory budget is a setting, not a wall.
- Image, video and language, from one engine. The same binary that runs a datacenter GPU runs a Pi; only the model and the memory budget change.