Alternatives to LiteRT-LM

llama.cpp is the foundational C/C++ library with 75K+ GitHub stars powering local LLM inference on consumer hardware. Provides optimized CPU and GPU inference for quantized models in GGUF format. Supports LLaMA, Mistral, Phi, Gemma, and most open-weight families. Features 2-8 bit quantization for reduced memory, multi-GPU support, context extension, grammar-constrained output, and an OpenAI-compatible API server. The engine behind Ollama and LM Studio.

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.

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.