TFLite vs CoreML iOS: 47ms Latency Gap Exposed

The Benchmark That Changed My Framework Choice

CoreML should destroy TFLite on iOS. Apple's own silicon, Apple's own framework, running on Apple's own Neural Engine. And yet here I am, staring at profiler output showing TFLite beating CoreML by 47ms on a MobileNetV3 classification task.

That's not a typo. TensorFlow Lite — Google's framework running as a guest on Apple hardware — outperformed the native solution on an iPhone 13 Pro. But before you close this tab thinking I messed up the benchmark, stick around. The full picture is more nuanced, and the winner depends entirely on which model architecture you're deploying.

Photo by Brett Jordan on Pexels

Test Setup: Same Model, Two Runtimes

I converted a MobileNetV3-Large classifier (1000 ImageNet classes) to both TFLite and CoreML formats. The source was identical: a PyTorch checkpoint exported to ONNX, then converted to each target format.

python
# TFLite conversion via tf.lite.TFLiteConverter
import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_saved_model('mobilenetv3_saved')

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