The Tool Desk
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1. Define the deployment limits before choosing a model or runtime
Write down what the device must do and what resources it can use. These constraints are the test the finished deployment must pass—not assumptions to make after conversion.
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- Task and quality: State the task, acceptable accuracy or task quality, and the kinds of inputs the model will encounter.
- Input shape and workload: Record representative input sizes, how often inference runs, and whether the device must handle unusually large or difficult inputs.
- Latency and throughput: Set the acceptable response time and, if relevant, the number of inferences the device must sustain.
- Memory and storage: Set separate ceilings for available RAM and model or application storage. Reserve room for the operating system and the rest of the application.
- Power and thermals: Define any power limit and consider whether sustained inference must work without overheating or reducing performance.
- Platform and hardware: Record the operating system, processor architecture, and available CPU, GPU, NPU or other accelerator.
There is no universal RAM threshold or “fast enough” model size: both depend on this envelope and the full inference workload.
2. Choose a runtime by compatibility, not by name alone
Start from the framework and model you have, then verify that the runtime can import or convert it, supports its operations, and has a suitable backend for the exact target platform. A model that converts successfully may still use unsupported operations or fail to execute on the intended accelerator.
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| Runtime | Documented deployment path | What to verify for your target |
|---|---|---|
| LiteRT | Google AI Edge documentation describes conversion and execution tooling. Its getting-started overview identifies CompiledModel as the current recommendation for performance-focused applications, while Interpreter remains available for compatibility. | Conversion and operator support, target-platform availability, backend support for the model’s operations and data types, and whether CompiledModel or Interpreter fits the application. |
| ExecuTorch | PyTorch documentation describes exporting a model to a graph, compiling it into an executable program, and running that program through the device runtime. The workflow includes compile-time optimization and memory planning. | Export support, operator and backend coverage, target-platform support, and whether the compiled program can run its required operations on the selected hardware. |
| ONNX Runtime | ONNX Runtime documentation describes a cross-platform deployment path for IoT and edge devices using hardware-specific libraries. | Model and operator support, availability of the relevant hardware library on the device, and execution behavior for the chosen backend. |
These descriptions establish different deployment paths, not a universal winner. Compare the candidates on the same model and representative workload, including conversion effort, peak memory, cold-start time, inference latency, accuracy, power use and maintenance needs. Consult each project’s official documentation for current platform and operator support; these details change over time.
3. Export or convert the model, then check that it actually runs
Use the selected runtime’s documented export or conversion route. In ExecuTorch’s documented workflow, the model is exported to a graph, compiled into an executable program, then run using the device runtime. LiteRT provides conversion and execution tooling; ONNX Runtime provides a cross-platform route with hardware-specific libraries.
After conversion, test the resulting model on the intended runtime and backend with representative inputs. Check for unsupported operations, conversion errors, unexpected output differences and accelerator fallbacks. A converted file alone does not establish that the model is compatible with the target or that it will meet memory and latency limits.
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4. Optimize in measured steps
Try quantization when its trade-offs fit the task
Quantization can reduce model storage and runtime memory and simplify arithmetic. The available formats and resulting performance depend on the runtime and hardware, and reduced precision can affect model accuracy or task quality. Begin with an appropriate post-training quantization option when supported, then compare the converted model against the original using representative data and the task’s acceptance criteria.
If quality falls below the required level, investigate another supported quantization recipe or quantization-aware training. Do not assume a format will accelerate inference: confirm that the chosen backend supports it and measure the actual device execution.
Treat pruning and clustering as compression options, not automatic speedups
TensorFlow’s model optimization documentation says pruned models remain the same size on disk and have the same runtime latency, while becoming more compressible. Pruning or clustering may help with compression, but verify the actual artifact size and runtime behavior. Neither technique by itself proves that an application will use less peak memory or run faster.
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5. Enable acceleration only when the full model path supports it
Check that the intended CPU, GPU, NPU or other accelerator supports the model’s operations and quantized data types through the selected runtime backend. Unsupported operations may fall back to another processor or prevent execution; a partially accelerated model may not deliver the expected result.
Test the actual backend rather than inferring support from the device’s hardware specifications. Record which backend ran the model and whether any operations used a fallback, so measurements reflect the deployment you intend to ship.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Benchmark on the target device
Run tests on the actual device with representative and worst-case inputs. Measure the whole deployment, not just the model file or a conversion-time estimate.
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- Peak runtime or resident memory: Confirm the application fits its RAM ceiling under the workload, including the runtime and model execution.
- Cold-start time: Measure the delay from launch or model loading to usable inference.
- Steady-state latency and throughput: Measure repeated inference under realistic conditions, not only a single run.
- Accuracy or task quality: Check outputs against the original model and the task’s required quality.
- Power and temperature: Observe sustained operation as well as initial performance; a short test may not reveal thermal or power limits.
Repeat these checks after changing the model, runtime, conversion settings, firmware or backend. A smaller stored model does not by itself show that peak runtime memory will fit.
7. Keep enough detail to reproduce a passing deployment
For each tested build, record the model and runtime versions, export and quantization settings, device and operating system, selected backend, test inputs, and measurement method. This makes it possible to identify what changed when a later build behaves differently.
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