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Deploy machine learning models on mobile and IoT devices

TensorFlow Lite is an open source deep learning framework for on-device inference.

See the guide

Guides explain the concepts and components of TensorFlow Lite.

See examples

Explore TensorFlow Lite Android and iOS apps.

See models

Easily deploy pre-trained models.

How it works
Pick a model

Pick a new model or retrain an existing one.


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


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


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

Solutions to common problems

Explore optimized models to help with common mobile and edge use cases.

Image classification

Identify hundreds of objects, including people, activities, animals, plants, and places.

Object detection

Detect multiple objects with bounding boxes. Yes, dogs and cats too.

Smart reply

Generate reply suggestions to input conversational chat messages.

Community participation

See more ways to participate in the TensorFlow community.

TensorFlow Lite on GitHub 
Ask a question on StackOverflow 
Community discussion forum 
Share your TensorFlow Lite story
News & announcements
Jun 11, 2019 
TensorFlow Model Optimization Toolkit — Post-Training Integer Quantization

Post-training quantization produces integer quantized models that are fast on CPU, and can be deployed in accelerators

Jan 16, 2019 
TensorFlow Lite Now Faster with Mobile GPUs (Developer Preview)

Run inference on GPU can improve inference up to ~4x on Pixel 3.

Aug 19, 2018
AI in motion: designing a simple system to see, understand, and react in the real world

In this three-part blog series, we walk through how we designed a simple system that demonstrates how computers can see, understand, and react to the world around us.

Sep 18, 2018 
Introducing the Model Optimization Toolkit for TensorFlow

The new optimization toolkit in TensorFlow provides a suite of techniques that developers—both novice and advanced—can use to optimize machine learning models.