ExecuTorch

Google's lightweight ML framework for mobile and embedded

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.

Intel's open-source AI inference optimization toolkit

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.

Cross-platform high-performance ML inference engine

ONNX Runtime is Microsoft's open-source inference engine for machine learning models in ONNX format. It delivers cross-platform acceleration via execution providers for NVIDIA CUDA, TensorRT, DirectML, CoreML, OpenVINO, and more. Supports training acceleration, quantization, and GenAI workloads. Used in production across Windows, Azure, Office 365, and thousands of applications with pip-installable Python and native C++/C#/Java APIs.

Run LLMs natively on any device with ML compilation

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.

First commercially viable 1-bit LLMs that are 14x smaller and 8x faster

PrismML Bonsai delivers the first commercially viable 1-bit large language models with 8B, 4B, and 1.7B parameter variants. The 8B model runs in just 1GB of RAM versus 16GB for standard FP16 models, achieving 44 tokens per second on iPhone. Backed by $16.25M from Khosla Ventures and released under Apache 2.0, Bonsai makes capable LLMs practical for edge devices and resource-constrained environments.