If you're shipping on-device ML on iOS in 2026, you are picking between MLX (Apple-native, Apple Silicon optimised) and TensorFlow Lite (cross-platform, battle-tested). They trade differently than you expect.
MLX's killer feature
MLX was built for Apple Silicon. It uses the unified memory model, runs natively on the ANE when possible, and has PyTorch-like syntax. Inference speed on Apple hardware is routinely 2-3x faster than TFLite.
TFLite's killer feature
Ubiquity. If you need the same model on iOS, Android, and embedded devices, TFLite is the only complete answer. The tooling, community, and model zoo are ten years deep.
The Flutter question
If you're on Flutter, TFLite has the mature plugin (tflite_flutter). MLX integration from Flutter requires platform-channel bridging.
Conversion pain
Converting a model is almost never one-click. Budget a week per model for TFLite. MLX has fewer converters but the ones it has (LLM-focused) work reliably.
The pragmatic answer
LLM inference: MLX or the native flutter_gemma binding (which uses the platform-native runtime). Computer vision: TFLite. Custom models: start TFLite, move to MLX if Apple-exclusive and performance matters.
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