2 Repos
Running machine learning models within a live Android application environment.
Distinct from Android Runtime Execution: Distinct from general Android runtime execution by focusing specifically on AI model inference and tokenization.
Explore 2 awesome GitHub repositories matching mobile development · AI Model Execution. Refine with filters or upvote what's useful.
Genkit is an open-source framework for building AI-powered applications. It provides a unified interface for connecting to hundreds of generative AI models from multiple providers, enabling text, image, audio, and video generation through a single API. The framework structures multi-step AI interactions—including chat, retrieval-augmented generation, tool use, and agentic workflows—as composable, traceable flows with built-in streaming and state management. The framework distinguishes itself through a comprehensive developer toolkit that includes a command-line interface and a local developer
Provides a flow-based execution model for composable, traceable AI workflows with streaming and state management.
ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,
Loads serialized programs and runs inference with a lightweight C++ runtime on edge devices.