Cactus is a low-latency, energy-efficient AI inference framework designed specifically for mobile devices and wearables, enabling advanced machine learning capabilities directly on-device. It provides a full-stack architecture composed of an inference engine, a computation graph system, and highly optimized hardware kernels tailored for ARM-based processors. Cactus emphasizes efficient memory usage through techniques such as zero-copy computation graphs and quantized model formats, allowing large models to run within the constraints of mobile hardware. It supports a wide range of AI tasks including text generation, speech-to-text, vision processing, and retrieval-augmented workflows through a unified API interface. A notable feature of Cactus is its hybrid execution model, which can dynamically route tasks between on-device processing and cloud services when additional compute is required.
Features
- OpenAI-compatible APIs for chat, vision, and multimodal AI tasks
- Zero-copy computation graph optimized for mobile environments
- ARM SIMD kernel optimizations for efficient on-device inference
- Hybrid routing between local execution and cloud fallback
- Support for quantized models with low memory and battery usage
- Cross-platform bindings for mobile and application frameworks