Performance-Adaptive Deployment
Deployment-ready availability via measurement-based model selection
ZETIC.MLange provides the best user experience by benchmarking the performance of AI models on a pool of real-world devices. It benchmarks different processors from various manufacturers, including CPU, GPU, and NPU. Based on these results, MLange ensures optimal performance on the deployed user target device, regardless of the device type.
Objective
Guarantee optimized target library installation
ZETIC.MLange ensures Deployment-Ready Availability by rigorously validating performance across a global cluster of physical devices. We do not rely on heuristic rules or theoretical specs. Instead, we perform On-Target Performance Measurement to empirically determine the optimal model for every single device.
Performance-Based, Not Rule-Based
Traditional deployment uses static rules (e.g., "Use GPU if version > X"). This often fails due to driver fragmentation and thermal throttling.
ZETIC.MLange is different. We establish ground truth by measuring:
Actual Latency
Millisecond-precision inference time measured on physical devices.
Throughput
Real-world tokens/frames per second capacity.
Stability
Continuous execution reliability under thermal stress.
Based on this data, we identify the specific model binary that yields the highest performance for each specific device model.
Global Deployment Assurance
By testing against the fragmented landscape of Android and iOS hardware, we guarantee:
Guaranteed Runtime Compatibility
Your model is rigorously verified to load and execute correctly on every fragmentation of Android and iOS targets.
Adaptive Binary Selection
The runtime dynamically resolves the exact quantized binary that yields maximum throughput for the specific NPU chipset.
Optimal Deployment Strategy
Deployment decisions are governed by deterministic benchmark data from our device farm, eliminating theoretical guesswork.
Validation Workflow
Provision Test Environment
We instantiate an isolated, on-device runtime environment mirroring the target OS and hardware configuration.
Distributed Workload Execution
The compilation artifacts, model metadata, and test vectors are dispatched to a distributed device farm. We execute the model on over 200 physical devices to capture real-world metrics.
Telemetry Analysis & Winner Selection
We aggregate the performance data to select the "Winning Model" for each device identifier.
YOLOv11 Benchmark Results
| Device | SoC Manufacturer | CPU | GPU | NPU | Remarks |
|---|---|---|---|---|---|
| Samsung Galaxy A34 | MediaTek | 172.08 ms | 96.38 ms | 249.41 ms | x1.79 |
| Samsung Galaxy S22 5G | Qualcomm | 79.76 ms | 36.99 ms | 8 ms | x9.97 |
| Samsung Galaxy S23 | Qualcomm | 89.56 ms | 27.5 ms | 5.24 ms | x17.09 |
| Samsung Galaxy S24+ | Qualcomm | 60.43 ms | 21.46 ms | 3.92 ms | x15.42 |
| Samsung Galaxy S25 | Qualcomm | 53.69 ms | 17.22 ms | 3.72 ms | x14.43 |
| Apple iPhone 12 | Apple | 123.12 ms | 22.73 ms | 3.51 ms | x35.08 |
| Apple iPhone 14 | Apple | 111.29 ms | 15.75 ms | 3.75 ms | x29.68 |
| Apple iPhone 15 Pro Max | Apple | 96.36 ms | 7.72 ms | 2.05 ms | x47.00 |
| Apple iPhone 16 | Apple | 102.09 ms | 7.9 ms | 1.9 ms | x53.73 |
Source: Original Benchmark Report
Automatic Distribution
When a user installs your app, the ZETIC.MLange Runtime automatically fetches the "Winning Model" for their device. This creates a seamless, high-performance experience without any manual configuration from the developer.
Advanced Telemetry Report (Premium)
We execute profiling for all users to guarantee the best performance of the On-device AI app. However, detailed profiling results are currently available for Starter users only.
Please contact us for more information.