LiteRT.js, Google's high performance Web AI Inference
How LiteRT.js benefits web developersWith LiteRT.js, web developers can integrate models into their apps written in JavaScript or TypeScript to handle complex tasks like text generation, object detection, and audio processing entirely client-side. To help you unlock these AI capabilities easily, here are the main highlights of LiteRT.js:1.PyTorch conversion & tailored quantizationWith LiteRT Torch, PyTorch models can be converted in a single step, making them instantly ready to leverage advanced browser-based hardware acceleration. Get started today by following the LiteRT Torch guide.For further optimization, AI Edge Quantizer allows you to configure tailored quantization schemes across different model layers.
- ▪How LiteRT.js benefits web developersWith LiteRT.js, web developers can integrate models into their apps written in JavaScript or TypeScript to handle complex tasks like text generation, object detection, and audio processing entirely clien
- ▪To help you unlock these AI capabilities easily, here are the main highlights of LiteRT.js:1.PyTorch conversion & tailored quantizationWith LiteRT Torch, PyTorch models can be converted in a single step, making them instantly ready to lever
- ▪Get started today by following the LiteRT Torch guide.For further optimization, AI Edge Quantizer allows you to configure tailored quantization schemes across different model layers.
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How LiteRT.js benefits web developersWith LiteRT.js, web developers can integrate models into their apps written in JavaScript or TypeScript to handle complex tasks like text generation, object detection, and audio processing entirely client-side. As LiteRT.js shares a unified cross-platform stack with LiteRT, your web applications automatically benefit from the latest performance upgrades, quantization improvements, and hardware optimizations developed for Android, iOS, and desktop.By leveraging LiteRT's lowering flow and runtime, you get simple conversion of models from a variety of Python ML frameworks and native hardware acceleration across all major accelerators (CPU / GPU / NPU).
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