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An accelerated model can still run some operations on the CPU when its GPU, NPU, or other execution backend does not support every operation or configuration. Many runtimes split the graph: supported sections run on the accelerator, while unsupported sections fall back to the CPU. That is partial acceleration, not proof that the accelerator is inactive.
Whether this helps depends on which operations fall back, how often data crosses between devices, synchronization costs, and the workload. First find out where the runtime placed the operations; a setting that enables an accelerator does not prove that your model runs there end to end.
What CPU fallback means
CPU fallback is a compatibility behavior: when a backend cannot execute an operation, a runtime or backend may route it to the CPU so the model can continue running. PyTorch describes this mechanism for operations unsupported by a new accelerator backend, while also noting performance as a trade-off. Its documentation describes selective fallback registration, rather than requiring every operation to use a global fallback rule. The exact behavior depends on the backend implementation. PyTorch operator registration documentation
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Why an accelerator-enabled model can still be slow
Accelerator availability, operator coverage, and end-to-end speed are separate questions. The selected provider may support some operators but not others, or support an operator only for particular shapes, types, or parameters. The runtime may therefore divide the graph into device-specific sections.
Those divisions can add transfers and synchronization between CPU and accelerator. TensorFlow Lite’s NNAPI delegate documentation warns that this overhead can make split execution slower than running the whole network on CPU. It does not mean every fallback operation makes a model slower; the result depends on the graph, target device, and workload. TensorFlow Lite NNAPI delegate documentation
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For that reason, do not infer a speedup from a GPU or NPU setting, or from seeing accelerator activity. Measure the actual model on the target hardware, including the work required to move data between devices.
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- Record the execution context. Note the framework and runtime versions, selected execution provider or delegate, target device, and model. Support and placement can vary among these.
- Inspect runtime placement diagnostics. In Torch-TensorRT, use the documented dry-run output to identify fallback operators and the boundaries between TensorRT and PyTorch partitions. Its troubleshooting documentation notes that extensive PyTorch fallback indicates low TensorRT coverage and can correlate with slow execution. Torch-TensorRT user guide Torch-TensorRT troubleshooting
- For ONNX Runtime, examine placement logs. The available performance-tuning guide recommends verbose logging to inspect the runtime’s placement decisions. That guide is in a legacy Microsoft repository fork, so confirm any version-specific instructions against the active ONNX Runtime documentation for the version you use. ONNX Runtime performance-tuning guide (legacy fork)
- Match the operation to backend support. Once diagnostics identify fallback operations, check whether the selected backend supports each exact operation with the model’s shapes, data types, and parameters. Do not assume that support for an operator name covers every configuration.
- Benchmark comparable runs. Compare CPU-only and accelerated execution on the same target using the same inputs and measurement method. Include warm-up and transfer costs where relevant, and measure end-to-end latency rather than relying on accelerator utilization alone.
What to do after you identify fallback
- If the model is correct and fast enough: fallback may be an acceptable compatibility trade-off. Keep the configuration that meets your latency and correctness needs.
- If diagnostics show a coverage gap: investigate support for the exact operation and configuration in your selected provider or delegate. Consider a graph or runtime change only when the observed placement points to a specific issue.
- If CPU-only execution is faster: compare the cost of device-boundary transfers and synchronization with the benefit of accelerated sections. A split graph is not automatically better than a single-device path.
- After each change: check correctness and inspect placement again, then repeat the same benchmark. This distinguishes a real improvement from a changed execution path that merely looks more accelerated.
How to compare execution paths
Use these criteria when evaluating fallback-enabled execution, an accelerator path with broader operator coverage, and CPU-only execution. There is no universal coverage percentage or speedup: useful values must be measured for the specific model, device, runtime, and benchmark method.
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| Criterion | What to establish |
|---|---|
| Operator coverage | Which graph sections execute on the intended accelerator, and which run on CPU. |
| Partitions and boundaries | How many device-specific sections the graph has and where data must move between them. |
| End-to-end latency | Measured performance on representative inputs and the target device, using the same method for each path. |
| Correctness and compatibility | Whether the execution path preserves the model behavior required by your application. |
| Operational complexity | Whether reducing fallback requires graph changes, model conversion, or provider-specific configuration. |
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