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title: Google Launches AI Inference “LiteRT.js“ | daily.dev
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# Google Launches AI Inference “LiteRT.js“

**[C\# Corner](https://daily.dev/sources/csharpcorner)** · 2 min read · 2 upvotes · 1 comments

## Summary

Google has launched LiteRT.js, a high-performance JavaScript runtime that enables machine learning and generative AI inference directly in web browsers using local hardware acceleration via WebGPU. It is part of the LiteRT ecosystem (successor to TensorFlow Lite) and extends browser AI with fully local execution, zero inference costs after model download, offline support, and a simplified model conversion pipeline from PyTorch, TensorFlow, and JAX. LiteRT.js is designed to complement rather than replace TensorFlow.js, allowing developers to swap only the model execution layer for improved performance while keeping existing preprocessing logic intact.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.c-sharpcorner.com/news/google-launches-ai-inference-litertjs>

## Questions this post answers

### What is LiteRT.js and how does it run AI models in the browser?

LiteRT.js is a JavaScript runtime from Google that runs machine learning and generative AI models directly inside web browsers using local hardware acceleration, primarily WebGPU with future WebNN support. It is part of the LiteRT ecosystem, the successor to TensorFlow Lite, and executes models fully on-device for lower latency, offline use, and no per-inference cloud costs after the model is downloaded.

_Developers weighing on-device versus cloud inference can track runtime releases like this on daily.dev._

### Can I use LiteRT.js together with TensorFlow.js in the same web app?

Yes, LiteRT.js is designed as a complementary runtime rather than a TensorFlow.js replacement. Developers can keep using TensorFlow.js for preprocessing and postprocessing while swapping only the model execution layer to LiteRT.js, so migration typically means replacing model loading code while leaving most application logic unchanged.

_Teams planning a TensorFlow.js migration path can follow runtime updates like this via daily.dev._

### How do I convert a PyTorch model to run with LiteRT.js in the browser?

LiteRT.js supports converting models directly from PyTorch, TensorFlow, or JAX into the .tflite format without the complex conversion chains typically required for TensorFlow.js workflows. This simplified pipeline is one of the key improvements over earlier browser-based inference approaches, making it easier to bring existing models to web-native AI experiences.

_Developers migrating models across frameworks can follow tooling changes like this on daily.dev._

## Community discussion

Top comments from developers on daily.dev.

**@aleksei\_aleinikov** · 0 upvotes

> A useful step for browser-based AI. Local inference with WebGPU can reduce latency, improve privacy, and remove ongoing inference costs.

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#tensorflow](https://daily.dev/tags/tensorflow), [#webgpu](https://daily.dev/tags/webgpu)

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