VeizikNVIDIA, Apple Silicon and edge builds are available now.Release notes →
Solutions

What you can build on Veizik, honestly.

Use-cases drawn from what the engine does today — server, on-device, edge, multimodal and control. Each is marked Verified, Supported or On the roadmap, from the ledgers, never from a promise.

Server inference

Run your own checkpoints on your NVIDIA GPUs.

One native engine on NVIDIA CUDA, Ampere through Blackwell. The checkpoint you downloaded is the one it runs — no Python environment on the box, no converted model copy, no framework stack to patch.

Verified

No Python on the server

The engine is a compiled binary. It reads the published weights and executes them natively — nothing to pip-install and nothing to keep in sync.

Runs where the data is

Generation happens on your own hardware. Prompts, weights and outputs stay on the machine; only account, activation and downloads reach the network.

Measured, not projected

Every performance figure on the site is taken on named hardware with a released binary, bound to that build’s checksum.

Veizik for NVIDIA → NVIDIA results → Download →

A tranquil alpine lake at sunrise with mirror reflections of snow-capped peaks and a pine-forest shoreline — AI-generated with FLUX.1-schnell on veizik (NVIDIA RTX 3090).
FLUX.1-schnellNVIDIA RTX 3090

Drawn natively by Veizik on an NVIDIA RTX 3090 — FLUX.1-schnell output, not a benchmark.

On-device inference

A model on the machine in front of you.

Language models run on Apple Silicon today, M1 through M4, as one Metal binary. On Android edge SoCs the engine is measured on the board and deployment is still pending — shown as roadmap, not as shipped.

Verified
Apple SiliconAvailable

M1 – M4 · Metal

Open language models run on a Mac as a single binary, offline after activation.
Veizik for Apple Silicon →
Mobile deviceDeployment pending

On-device Android · measured on the board

Measured on-device; a customer deployment is not yet available. We do not claim it ships until it does.
Edge & mobile hardware →
Edge AI

Inference on the board, not in a datacentre.

The engine is pure-native and dependency-free, so it runs on CPU edge boards — Raspberry Pi 5 and Zero 2 W — with no accelerator required. Language, on-device vision, image and video run on the board.

Supported
Runs on the CPUArm NEON native execution — no GPU, no NPU dependency to ship around.
Offline by constructionAfter activation a device runs with no connection; restarting ends the session lease.
Sealed, device-bound packagesEach build is a single sealed package bound to the device it was activated on.

Veizik on the edge → SoC hardware → Edge models →

A lighthouse on a cliff at sunset under a dramatic sky, generated prompt-to-image entirely on a $15 Raspberry Pi Zero 2 W.
SDXS-512-DreamShaperRaspberry Pi Zero 2 W
A snow-capped mountain reflected in a calm lake at sunset, rendered by veizik on a Raspberry Pi 5.
SDXS-512-DreamShaperRaspberry Pi 5
A cozy Tokyo alley at night with neon reflections on wet pavement, rendered by veizik on a Raspberry Pi 5 with Stable Diffusion 1.5 and a few-step LCM-LoRA.
Stable Diffusion 1.5 + LCM-LoRARaspberry Pi 5
A moody forest path in autumn fog with volumetric light, rendered by veizik on a Raspberry Pi 5 with Stable Diffusion 1.5 and a few-step LCM-LoRA.
Stable Diffusion 1.5 + LCM-LoRARaspberry Pi 5

Shown as what the engine draws on the board — output, not a benchmark. Each was rendered by Veizik natively on a Raspberry Pi, with no accelerator.

Multimodal

One runtime for language, image and video.

The same engine runs large and small language models, text-to-image and image-to-image, and text-to-video and image-to-video. One install, one command surface, one set of licence rules — across every class it supports.

Verified
Robotics & control

On-device control policies, weights included.

Reinforcement-learning control policies run as native on-device engines. These are the four policies that ship with their weights and are verified on hardware; Jetson control policies are on the roadmap.

Verified

Ships with the weights

The control policies are distributed with their weights (Stable-Baselines3 RL Zoo, MIT) — install and run, no separate download.

Deterministic execution

A control step is reproducible on the shipped build, the same discipline every published figure is held to.

Edge-GPU preview next

Jetson AGX Orin is a render-proven preview for generative models; Jetson control policies are on the roadmap and marked as such.

Control policies → Jetson hardware →

Enterprise deployment

Device-bound, local by design.

Activation binds a licence to each machine, the engine ships sealed and hardened, and generation stays on your hardware. Fleet-scale controls are in active development and are shown as roadmap, not as shipped.

Supported
Activated per deviceApprove a machine once in the browser; every published model then runs on it, offline, with no per-model licence.
A protected engineThe compiler and kernels ship sealed and hardened, so a deployment does not expose them.
AI transparency, built inEvery image or video build marks its output as AI-generated — in the file and in the pixels — with no switch to turn it off (EU AI Act Art. 50; Korea AI Framework Act Art. 31).
Fleet controls — on the roadmapMulti-GPU execution, a central licence server and an audit log are in development. We will say they ship when they do.

Security & licensing → Licensing → Talk to us about deployment →

Get started

Pick a platform and run a model.

Everything above runs from the same install. Start with your hardware, or read how the engine works.

Download → Pick your device → How it works →