Generative ai system

Abstract

A method for running a multimodal generative artificial intelligence (AI) model includes transforming model parameters of a trained multimodal generative AI network from floating-point precision to selected bit-depth representations including 16-bit, 8-bit or 4-bit integers, wherein quantization comprises minimizing representation error and preserving semantic features across text, image, video, audio, or sensor modalities to reduce memory footprint and computational complexity; packaging the quantized model parameters and network architecture into a compressed deployment bundle; transmitting said bundle to one or more edge devices, wherein model compatibility and runtime configuration for heterogeneous device hardware are validated prior to installation; and conducting AI inference operations on the deployed edge devices with modular lightweight neural network layers, on-device caching of intermediate results, and batched or streaming inference, wherein inference on multimodal inputs is completed without exceeding predetermined device memory or compute constraints while maintaining generative accuracy.