ZETIC.MLange API

We enable True On-Device AI by making NPU acceleration accessible and effortless:

Fully Automated NPU Utilization

No manual driver management. We handle the hardware abstraction.

Unified End-to-End Workflow

From model training to on-device deployment in one pipeline.

Heterogeneous Hardware Orchestration

Unified support for heterogeneous NPUs across all Android and iOS devices.

Rapid Deployment

Ship production-ready AI in hours, not months.

Quick Start

Generate Model Key and Personal Key

Go to mlange.zetic.ai to generate your keys.

1. Upload & Auto-Compilation

Simply upload your model file to our dashboard. ZETIC.MLange automatically analyzes, quantizes, and compiles the graph for heterogeneous NPU targets in the background.

Supporting model format:
- Pytorch Exported Program(.pt2)
- Onnx Model(.onnx)
- Torchscript Model (.pt) (To Be Deprecated)

Generate Model Key

2. Get Your Deployment Keys

Once optimized, specific keys are provisioned for your project.

Model Key: The unique identifier for your hardware-accelerated binary.
Personal Key: Your secure credential for on-device authentication.

Copy Personal Key

For comprehensive details on key provisioning and security policies, please consult:

EZ Tip: You don't have to remember the keys

The MLange Dashboard provides ready-to-use source code with your keys already pre-filled. You can simply copy and paste it directly into your project!

Integrate SDK & Run Inference

Initialize the ZeticMLangeModel with your keys to trigger hardware-accelerated execution.

    // Zero-copy NPU Inference
    val model = ZeticMLangeModel(CONTEXT, "YOUR_PERSONAL_KEY", "YOUR_MODEL_NAME")
    model.run(YOUR_INPUT_TENSORS)

    // Zero-copy NPU Inference
    let model = try ZeticMLangeModel("YOUR_PERSONAL_KEY", "YOUR_MODEL_NAME")
    model.run(YOUR_INPUT_TENSORS)

Explore Documentation

What is ZETIC.MLange?

Learn about ZETIC.MLange and its key features

How it Works

Understand the workflow and architecture

Model Profiling

Global device benchmark and optimization

Examples

Explore real-world implementation examples

Need Help?

Since we are developing rapidly, please contact ZETIC.ai for any kind of issues or questions.