TensorFlow Lite vs ONNX: Choosing the Right Edge Runtime

When deploying AI to edge devices, runtime choice significantly impacts performance, compatibility, and development experience. Here's our guide to choosing between TensorFlow Lite and ONNX.

Overview

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TensorFlow Lite Google's solution for mobile and edge devices. Tightly integrated with the TensorFlow ecosystem.

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ONNX Runtime Microsoft's open standard for model interchange. Framework-agnostic approach.

Feature Comparison

FeatureTensorFlow LiteONNX Runtime |---------|-----------------|--------------| Model SourceTensorFlowAny (PyTorch, TF, etc.) QuantizationExcellentGood Operator Support150+170+ Hardware AccelerationCoral, GPU, NNAPIDirectML, CUDA, TensorRT Model SizeSmallerSlightly larger CommunityVery largeGrowing

When to Choose TensorFlow Lite

- TensorFlow models: Native conversion without intermediate steps - Mobile apps: Best Android/iOS integration - Google hardware: Coral TPU optimization - Quantization priority: Superior int8 support - Smaller models: Aggressive optimization

When to Choose ONNX

- PyTorch models: Direct export support - Multi-framework: Need to deploy models from various sources - Windows/Azure: DirectML acceleration - NVIDIA hardware: TensorRT integration - Flexibility: Framework-agnostic future-proofing

Our Approach at Tesan AI

We support both runtimes, choosing automatically based on:

1. Source framework: PyTorch models → ONNX, TensorFlow → TFLite 2. Target hardware: Coral → TFLite, NVIDIA → ONNX/TensorRT 3. Performance requirements: Benchmark both, choose faster 4. Size constraints: TFLite often wins for smallest models

Conversion Tips

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TensorFlow to TFLite

converter = tf.lite.TFLiteConverter.from_saved_model(model_path)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

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PyTorch to ONNX

torch.onnx.export(model, dummy_input, "model.onnx",
                  opset_version=13,
                  input_names=['input'],
                  output_names=['output'])

Benchmarks

On Raspberry Pi 4 (MobileNetV2 classification):

RuntimeLatencyMemoryAccuracy |---------|---------|--------|----------| TFLite FP3289ms14MB71.8% TFLite INT834ms4MB71.1% ONNX FP3295ms16MB71.8% ONNX INT841ms5MB71.0%

Conclusion

There's no universal winner. The best choice depends on your specific constraints. With Tesan AI, you don't have to choose—we handle runtime selection automatically for optimal performance on each target device.