CoreML Metal GPU delivers 9ms vs TFLite's 21ms
Running MobileNetV2 inference on an iPhone 15 Pro with CoreML Metal takes 9ms. The same ONNX model converted to TFLite and run with GPU delegation? 21ms. On the Pixel 8, TFLite manages 14ms with NNAPI acceleration while CoreML isn't even an option.
This isn't a fair fight — Apple controls the entire stack from silicon to framework. But if you're shipping a mobile AI app in 2026, you need real numbers on both platforms before you commit to a conversion pipeline.
I converted the same pretrained MobileNetV2 model to both CoreML (.mlmodel) and TFLite (.tflite), ran identical inference workloads, and measured GPU latency with proper warm-up. The gap is bigger than the marketing claims suggest, and it comes down to three things: Metal vs Vulkan GPU access, compiler optimization depth, and how much each framework actually knows about the chip it's running on.
The test setup: same model, different conversion paths
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