From model optimization to NPU deployment, complete the entire workflow in one place.
Upload a model, choose a target device, and go from optimization to a runnable package in one flow.
Start a runView workflowDefine the model, targets, and optimization goal.
Place precision by layer importance.
Build for both target runtimes.
| Target | Runtime | Precision | Status |
|---|---|---|---|
| Snapdragon NPU | QNN | INT8 / W4 | Ready |
| Apple Neural Engine | Core ML | INT8 / W4 | Ready |
Compare quality and performance on device.
| Metric | Baseline | Optimized |
|---|---|---|
| Accuracy | 100% | 99.37% |
| Memory | 100% | 73% |
| Inference | 1.0× | 1.5× |
Everything needed to run on the device.
Move from a single recipe to a runnable package without switching between vendor-specific tools.
Define the model, target device, accuracy goal, and memory constraints in one executable recipe.
Configuration
Precision plan
Opt.Studio brings repetitive, specialized on-device conversion work into one product, so model teams can focus on their models and services instead of learning another vendor SDK.
Analyze layer sensitivity and real data distributions to assign the precision each layer needs while preserving the target accuracy.
Generate Qualcomm QNN and Apple Core ML execution paths from one recipe. The workflow stays consistent as targets expand.
Validate accuracy, latency, memory, and power on real devices while recording results at every stage.
Apple Neural EngineOpt.Studio is not open yet. We are preparing the public release — tell us about your model and target device, and we will reach out when access opens.
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