iOS

NPU Acceleration, Simplified.

Leveraging the Neural Engine on iOS often requires deep knowledge of CoreML, Metal Performance Shaders, and low-level buffer management.

ZETIC.MLange abstracts this entire stack. We provide a unified Swift interface that handles the compilation, optimization, and execution on the Neural Engine automatically. No manual graph bridging required.

Prerequisites

Before you begin, make sure you have:

Model Key: Prepare using the Web Dashboard (SaaS) or CLI method
Personal Key: Prepare using the Web Dashboard (SaaS) method

Step-by-step Guide

Add ZeticMLange Package to Project

We use Swift Package Manager (SPM) to automatically resolve and link the binary dependencies required for NPU acceleration.

Click File → Add Package Dependencies in Xcode
Search for https://github.com/zetic-ai/ZeticMLangeiOS.git
Click Add Package

Select Target for ZeticMLange Package

Link the ZeticMLange library to your specific application target. This injects the Unified HAL runtime into your app bundle.

Select target in the Add to Target column
Click Add Package

Initialize and Run ZeticMLangeModel

Initialize the model to trigger the Zero-Copy Model Loader, which maps your model directly to NPU memory.

// (1) Load Zetic MLange model
// This handles model download (if needed) and NPU context creation
let model = try ZeticMLangeModel(personalKey: PERSONAL_KEY, name: PROJECT_NAME, version: VERSION)

// (2) Prepare model inputs
// Ensure input shapes match your model's requirement (e.g., Float32 arrays)
let inputs: [Tensor] = [] // Prepare your inputs

// (3) Run Inference
// Executes the fully automated hardware graph.
// No manual delegate configuration or memory syncing required.
let outputs = try model.run(inputs)

Sample Application

Please refer to MLange iOS sample app repository for complete sample applications and more details.

iOS