TensorFlow Lite is for mobile and embedded devices

TensorFlow Lite is the official solution for running machine learning models on mobile and embedded devices. It enables on‑device machine learning inference with low latency and a small binary size on Android, iOS, and other operating systems.

Many benefits

On-device ML inference is difficult because of the many constraints—TensorFlow Lite can solve these:
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    Performance

    TF Lite is fast with no noticeable accuracy loss—see the metrics.
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    Portability

    Android, iOS, and more specialized IoT devices.
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    Low latency

    Optimized float- and fixed-point CPU kernels, op‑fusing, and more.
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    Acceleration

    Integration with GPU and internal/external accelerators.
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    Small model size

    Controlled dependencies, quantization, and op registration.
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    Tooling

    Conversion, compression, benchmarking, power-consumption, and more.

How it works

build

Pick a model

Pick a new model or retrain an existing one.
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Convert

Convert a TensorFlow model into a compressed flat buffer with the TensorFlow Lite Converter.
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Deploy

Take the compressed .tflite file and load it into a mobile or embedded device.
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Optimize

[optional] Quantize by converting 32-bit floats to more efficient 8-bit integers or run on GPU.

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TensorFlow Lite users

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