Moondream is an open-source, small-footprint vision-language model (VLM) designed for developers to integrate visual understanding into edge applications and resource-constrained environments.

• Operates using a 1.6 billion parameter architecture that bridges a SigLIP-base-patch16-224 vision encoder with a Phi-1.5 or Phi-2 language backbone.
• Performs visual question answering (VQA), object detection coordinates, and image captioning with low latency on standard CPU and mobile hardware.
• Provides cross-platform compatibility through implementations in Python (PyTorch), C++ (llama.cpp), and ONNX for hardware-accelerated inference.
• Maintains a compact memory footprint of approximately 800MB when utilizing 4-bit quantization, enabling deployment on devices with limited VRAM.

The model is distributed under the Apache 2.0 license and supports local execution to ensure data privacy and offline inference without external API dependencies. It is optimized for integration into real-time monitoring systems, mobile assistive technologies, and automated metadata generation pipelines.