Run large language models on iOS and Android devices. Manage the entire process from quantization to deployment in one unified platform.
Challenges to overcome when running LLMs on mobile devices
GPTQ, QAT, Mixed Precision... Each model requires different configurations
iOS and Android each require different conversion tools and workflows
Reducing model size degrades accuracy, making it hard to find the optimal point
Every configuration change requires running long commands in the terminal
Handle all optimization tasks in one unified platform
Manage Qualcomm (Android) and Apple ANE (iOS) conversions from a single dashboard. One-click setup to monitoring.
Just enter your target model size and Automatic Mixed Precision finds the optimal quantization settings. No more manual layer-by-layer bitwidth adjustments.
Visualize quantization error (MSE), per-layer memory usage, and GPTQ errors to intuitively understand model quality.
Unified conversion pipeline supporting both iOS and Android
Snapdragon 8 Gen 2/3 | Android
iPhone / iPad / Mac
Tools supporting every stage of model optimization
From model management to experiment comparison, all in one place
Automatically find optimal quantization settings based on target size
Fine-grained bitwidth control for each layer
Visualize model quality to derive optimal settings
Complex optimization process made simple
Enter HuggingFace model ID or local path
Set target platform, device, and context length
Optimize with Automatic or Custom mode
Deploy DLC/mlpackage model to device
Verified optimization results across various models
| Model | Original Size | Optimized | Size Reduction | Quality Retained |
|---|---|---|---|---|
| LLaMA 3.2 1B | 2.4 GB | 620 MB | 74% | 98.5% |
| EXAONE 4.0 | 5.8 GB | 950 MB | 84% | 97.8% |
| Qwen 2.5 3B | 6.2 GB | 1.2 GB | 81% | 98.2% |
Stable and scalable architecture
React + TypeScript
FastAPI
Python
AIMET, QNN SDK, SeqMSE
CoreML, GPTQ, LUT Quantizer
Transformers
Model Loading
Runtime
Complex mobile LLM optimization made simple with GenKit