VEIZIK for business

Control from compiler to execution.

VEIZIK develops the compiler, runtime and device-specific execution paths behind its supported model deployments. The same four layers carry every one.

Discuss your deployment Back to overview →

Model inputYour checkpoint — weights you already hold

Compiler & runtimeNative code generation and the execution path

Memory & schedulingResidency, offload and competing-workload order

Native backendCUDA, Metal or an Arm CPU — no Python, no framework

By technology

One stack, seven levers.

Each technology states the constraint it addresses, the mechanism, the measured effect and whether it is a shipped product or a capability still in development. Quantified figures live on the linked evidence pages.

Native execution

Shipped

Constraint: A full AI framework is a large, fragile dependency to carry into a deployment.
Mechanism: The engine compiles your model to native code and runs it on the backend directly — no Python, no framework, no cuBLAS.
Measured effect: A signed package of a few megabytes; the libraries it links are listed in the manifest.

Its own stack, top to bottom →

Precision

Shipped per model

Constraint: Memory and bandwidth cap what a device can hold and move.
Mechanism: Compiler-level sub-byte precision, chosen per model and applied without a converted copy of the weights.
Measured effect: Fewer bytes per weight, with quality tracked against the full-precision reference.

Fewer bytes per weight →

Hypercache

Shipped for supported models

Constraint: Building a large model cold, every start, is slow.
Mechanism: A sealed snapshot of the prepared model is restored instead of built from scratch.
Measured effect: A warm first token in seconds, with a cold build as the automatic fallback.

Resident in a heartbeat →

Memory

Shipped

Constraint: A single generation can exceed the device’s memory.
Mechanism: Residency, offload and scheduling are designed around a fixed memory budget.
Measured effect: Peak device and host memory reported per model and context length.

Video that doesn’t run out of memory →

Resilience

Shipped for supported work

Constraint: A long-running job can be interrupted before it finishes.
Mechanism: The engine writes a checkpoint boundary it can resume from.
Measured effect: A resumed run matches one that never stopped, checked by output hash.

Resume from a saved state →

Real-time control

Shipped for the control policies

Constraint: AI inference and a control loop compete for the same device.
Mechanism: CPU control runs on an isolated core under real-time scheduling, separate from GPU inference.
Measured effect: Microsecond-class control-loop latency with zero missed deadlines over an hour, measured on a Jetson AGX Orin.

Real-time control on Jetson →

LimAxiom

In development — not released

Constraint: You want to understand the execution path before committing to a deployment.
Mechanism: Predict and co-simulate the execution path for a target device and model before it runs.
Measured effect: A research capability — no product figure is published, and it is not part of a release.

Capability state: research · Product availability: not released

Reviewing a specific constraint?

Tell us the limit you are trying to control — memory, startup, deadlines or precision — and we will point you at the measured evidence.

Discuss your deployment