Alternatives to ONNX Runtime

ExecuTorch is PyTorch's official solution for deploying AI models on mobile, embedded, and edge devices. It features a 50KB base runtime, 12+ hardware backends including Apple CoreML, Qualcomm QNN, ARM, and Vulkan, and native PyTorch export without format conversions. Powers Meta's on-device AI across Instagram, WhatsApp, Quest 3, and Ray-Ban Smart Glasses, supporting LLMs, vision, speech, and multimodal models.

TensorFlow Lite is Google's lightweight ML framework for deploying models on mobile and embedded devices. It supports quantization, GPU/NPU delegation, and runs on Android, iOS, Linux, and microcontrollers. Provides pre-trained models, model conversion tools from TensorFlow and JAX, and hardware acceleration via GPU, Hexagon DSP, and CoreML delegates. Powers on-device ML in billions of Google app installations.

OpenVINO is Intel's open-source toolkit for optimizing and deploying AI inference across CPUs, GPUs, and NPUs. It supports models from PyTorch, TensorFlow, ONNX, and TFLite, providing graph optimizations, quantization, and hardware-specific acceleration. The toolkit includes a GenAI API for LLM deployment and runs on Intel, ARM, and x86 platforms for edge, desktop, and cloud inference workloads.

MLC LLM is an open-source engine for deploying large language models natively across diverse platforms using machine learning compilation. It runs models on NVIDIA/AMD GPUs, Apple Silicon, mobile devices, and browsers via WebGPU without cloud dependencies. Features include OpenAI-compatible API, quantization support, and optimized backends for CUDA, Metal, Vulkan, and WebAssembly.