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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →WebGPU was 1.4× to 9.4× faster than four-thread WASM in one ONNX Runtime Web benchmark on an M4 Mac—but the result changed sharply by model and input size. It is evidence that WebGPU can help, not a speed guarantee for your app. Your browser, device, model, precision, input shape, startup cost, and CPU–GPU data transfers all matter.
What did the benchmark measure?
NullPointerZen reported the results on September 29, 2026, using ONNX Runtime Web 1.27.0 on an M4 Mac with Chromium 149 and 16 GB of memory. Each configuration ran in a fresh browser process; the author performed three repeats and defined steady state as the median of runs two through six. The WASM comparison used four threads. These are the author’s measurements and calculations, not an ONNX Runtime-maintainer benchmark or an independently reproduced test. Read the benchmark report.
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| Model and test input | WebGPU | Four-thread WASM | Reported WebGPU speedup |
|---|---|---|---|
| ISNet, INT8, 1024×1024 | 359 ms | 2,133 ms | 5.9× |
| ISNet, FP16, 1024×1024 | 209 ms | 1,960 ms | 9.4× |
| Real-ESRGAN x4v3, 184×184 tile | 331 ms | 485 ms | 1.5× |
| Real-ESRGAN x4v3, 120×120 tile | 150 ms | 211 ms | 1.4× |
The range is wide because these are different workloads, not repeated measurements of one interchangeable task. It should not be read as a typical expected gain across models. The author did not test Windows, discrete GPUs, phones, Safari, or Firefox, and did not provide an uncertainty interval.
Why the multiplier varies
The benchmark author suggests that larger convolution workloads may give a GPU more parallel work, while smaller workloads may be constrained by fixed overhead. That is an interpretation, not a measured cause: the report did not profile performance by operator. Its data supports the narrower point that model, precision, and input size can change the observed gap substantially.
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Startup also matters. The report notes that first-run WebGPU timings were higher than its steady-state results. An application that runs inference once may therefore have a different experience from one that repeatedly processes inputs. Record cold-start and steady-state latency separately.
When should you choose WebGPU or WASM?
Measure WebGPU for compute-intensive models
ONNX Runtime describes WebGPU as an option for more compute-intensive models or when using the client’s GPU. It may be worth testing on devices and browsers that support it, but provider selection alone does not establish that the whole model runs on the GPU. WebGPU supports only a subset of ONNX operators; unsupported nodes may run elsewhere, affecting performance. The execution-provider documentation explains how providers claim supported nodes or subgraphs. ONNX Runtime’s WebGPU tutorial and execution-provider documentation describe these behaviors.
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Keep WASM in consideration for lightweight models
ONNX Runtime says the default WASM provider can remain a good fit for very lightweight models when small binary size matters. Its performance guidance also recommends WASM when a GPU is unavailable or the model is very small. Compare actual application performance rather than assuming a GPU backend wins on every workload. See the performance-diagnosis guide.
How to make a fair comparison in your app
- Check support on the deployment target. ONNX Runtime’s browser/platform matrix lists WebGPU for Chrome and Edge on macOS and WASM across the browser columns it documents. Requirements vary by platform and browser version; verify the current support matrix for the devices and browsers you intend to support.
- Use the real model and workload. Test the production model, precision, input shape, batch size, and tile size. The benchmark’s results differed between model and tile configurations, so a result from a different workload may not predict yours.
- Request the intended provider explicitly. In the documented WebGPU setup, import from
onnxruntime-web/webgpuand create the session withexecutionProviders: ['webgpu']. Follow the official setup instructions for the runtime version you deploy. - Measure both startup and repeated inference. Keep first-run latency separate from steady-state results. State how many runs you use and how you summarize them; a median and a single best time are not equivalent.
- Include the full application path. Ordinary WebGPU session inputs and outputs are CPU-memory tensors copied to GPU memory and back. Those transfers can change end-to-end latency. If data is already on the GPU, or subsequent processing can remain there, ONNX Runtime’s IO binding may avoid transfers; consult the WebGPU tutorial.
- Check execution placement and diagnose bottlenecks. A WebGPU provider setting does not prove every operator ran on the GPU. Inspect diagnostics and consider fallback behavior when interpreting timings. ONNX Runtime’s diagnosis guide covers performance investigation.
- Consider graph capture only when its conditions fit. ONNX Runtime presents graph capture as a possible optimization when shapes are static and all kernels run on WebGPU. Dynamic inputs or CPU fallback can make that approach unsuitable; see the graph-capture guidance.
What the figures do—and do not—tell you
The reported 1.4×–9.4× gains apply to one author’s M4 Mac and Chromium 149 test with ONNX Runtime Web 1.27.0, under the stated model and timing conditions. They do not establish a cross-platform average, a result for every ONNX model, or an end-to-end application improvement. The report’s author put the practical recommendation plainly: “Before you quote a WebGPU multiplier, run your own model at the input size you really use, and write first run and steady state down as two separate numbers.”
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