ZETIC Melange

Deploy any AI model to iOS and Android, automatically optimized for CPU, GPU, and NPU.

Deploy any AI model to iOS and Android with automatic NPU acceleration. No hardware expertise required. Just upload your model, integrate the SDK, and ship.

Naming Convention

We use the name Melange for the product. However, you will see MLange in the source code and library names (e.g., ZeticMLangeModel).

Quick Start

Generate Model Key and Personal Key

Go to Melange Dashboard to generate your keys.

1. Upload & Auto-Compilation

Simply upload your model file to our dashboard. Melange 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:

The Melange 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.

val model = ZeticMLangeModel(this, PERSONAL_KEY, "Steve/YOLOv11_comparison")
val outputs = model.run(inputs)

let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: "Steve/YOLOv11_comparison")
let outputs = try model.run(inputs)

The demo model key Steve/YOLOv11_comparison works without an account. Try it now in the Quick Start.

Why Melange?

⚡

Automated NPU Acceleration

No manual driver management. Melange handles hardware abstraction across Qualcomm, MediaTek, Samsung, and Apple chipsets.

🔗

Unified API

One API for Android and iOS. Write your integration once and deploy everywhere.

🔄

End-to-End Pipeline

From model upload to on-device execution. Automatic graph optimization, quantization, and compilation.

🚀

Production-Ready

Ship AI features in hours, not months. No C++, no OpenCL, no Metal shaders.

Performance

YOLOv11n on iPhone 16: CPU 102ms → NPU 1.9ms (54x faster)

Melange automatically selects the optimal hardware accelerator for each device and model, delivering peak performance without manual tuning. See full benchmark results.

Platform Support

Platform	SDK	NPU Targets
Android	Kotlin / Java via Maven	Qualcomm HTP/DSP, Google Tensor, MediaTek APU (Enterprise), Samsung Exynos DSP (Enterprise)
iOS	Swift via SPM	Apple Neural Engine (A11+)