🛰️ ZETIC.ai — On-Device AI for Every Device
Build. Deploy. Run. Anywhere.
ZETIC.ai helps AI engineers deploy models on any mobile device — without cloud GPU servers.
We transform your existing AI models into NPU-optimized, on-device runtimes in under 6 hours including from global device benchmark to runtime source code generation.
🚀 What We Do
ZETIC.MLange — our core platform — enables serverless AI by:
- Automated Conversion: Convert your PyTorch, ONNX, or TFLite model into a device-specific NPU library.
- Peak Performance: Up to 60× faster than GPU cloud inference, with zero accuracy loss.
- Broad Compatibility: Supports Android, iOS, Linux; MediaTek, Qualcomm, Apple NPUs — more coming soon.
- End-to-End SDK: From model optimization to app integration — no extra engineering required.
🛠 Key Features
- Zero GPU Costs — Replace expensive GPU cloud servers with free NPU power in devices.
- Full Privacy & Security — Data never leaves the device.
- Ultra-Low Latency — Real-time AI experiences, even offline.
- Cross-Platform — One model → All devices → Same performance.
📦 Example Use Cases
- 🎙 Speech Recognition (Whisper) — Real-time, offline transcription on mobile.
- 🦷 Dental AI Diagnostics — Instant tooth condition analysis via smartphone camera.
- 🏌️ Sports AI — On-device golf swing analytics.
- 🤖 On-Device LLMs — Chat & reasoning models running entirely offline.
📊 Benchmarks
| Device | Task | Cloud GPU | On-Device NPU | Speedup |
|---|---|---|---|---|
| iPhone 16 Pro | Whisper-Small | 1.2s | 0.07s | ×17 |
| Galaxy S24 Ultra | LLaMA-3-8B | 2.4s/token | 0.09s/token | ×26 |
YOLOv8n — NPU Latency (ms)
| Device | Manufacturer | CPU | GPU | CPU/GPU | NPU |
|---|---|---|---|---|---|
| Apple iPhone 16 | Apple | 126.27 | - | 8.98 | 2.03 |
| Apple iPhone 16 Pro | Apple | 122.23 | - | 7.54 | 1.69 |
| Samsung Galaxy S24+ | Qualcomm | 69.79 | 24.38 | 618.05 | 3.85 |
| Samsung Galaxy Tab S9 | Qualcomm | 107.78 | 30.39 | 344.42 | 5.21 |
| Samsung Galaxy S22 Ultra 5G | Qualcomm | 103.40 | 39.73 | 100.34 | 7.41 |
Whisper-tiny-encoder — NPU Latency (ms)
| Device | Manufacturer | CPU | GPU | CPU/GPU | NPU |
|---|---|---|---|---|---|
| Apple iPhone 16 | Apple | 552.13 | - | 44.49 | 19.01 |
| Apple iPhone 15 Pro | Apple | 527.78 | - | 43.13 | 19.40 |
| } Samsung Galaxy S23 | Qualcomm | 290.62ms | 169.82ms | 2,795.18ms | 86.88 |
| Samsung Galaxy S24+ | Qualcomm | 278.78 | 133.48 | 2619.56 | 106.44 |
| Samsung Galaxy S23 Ultra | Qualcomm | 308.82 | 170.08 | 2688.97 | 68.34 |
- You can get runtime source code and benchmark report of your model with ZETIC.MLange
👨🏻💻 Plug-and-play To Your App
The runtime SDK is also provided for your AI model with ZETIC.MLange
iOS Integration (Swift)
// import
import ZeticMLange
// ...
// (1) Load Zetic MLange model
let model = try ZeticMLangeModel("MLANGE_PROJECT_API_KEY")
// (2) Run model after preparing model inputs
let inputs: [Data] = [] // Prepare your inputs
try model.run(inputs)
// (3) Get output data array
let outputs = model.getOutputDataArray()
- Android Integration (Kotlin, Java)
// import
import com.zeticai.mlange.core.model.Target
import com.zeticai.mlange.core.model.ZeticMLangeModel
// ...
// (1) Load Zetic MLange model
val model = ZeticMLangeModel(this, "MLANGE_PROJECT_API_KEY")
// (2) Run model after preparing model inputs
val inputs: Array<ByteBuffer> = // Prepare your inputs
model.run(inputs)
// (3) Get output buffers of the model
val outputs = model.outputBuffers
📥 Try It Now
- MLange Dashboard: https://mlange.zetic.ai
- Demo Apps: App Store / Google Play
🧭 Supported Targets
- OS: Android, iOS, Linux
- NPUs: MediaTek, Qualcomm, Apple (more coming)
- Frameworks In: PyTorch, ONNX, TFLite
- Artifacts Out: NPU-optimized runtime libraries + SDK bindings (Kotlin, Java, Swift, Flutter, React Native)
📬 Contact Us
- Website: https://zetic.ai
- Email: [email protected]
- LinkedIn: linkedin.com/company/zetic-ai
ZETIC.ai — AI for All, Anytime, Anywhere.
Run your AI where it matters: on the device.