Mastering Edge AI on Raspberry Pi with LiteRT and Gemma

Google AI Edge presents LiteRT as an on-device inference runtime for deploying machine-learning models, including Gemma language models, on Raspberry Pi hardware without cloud connectivity. The post demonstrates a Raspberry Pi 5–based Reachy Mini robot that combines GPU-based YOLO object detection with CPU-based Moonshine speech recognition, Gemma reasoning and action generation, and text-to-speech to perceive and respond locally in real time. It describes several Gemma variants for constrained edge workloads and reports that Gemma 4 E2B reaches 99 prefill tokens per second, 9 decode tokens per second, and a 1,432 MB peak memory footprint on Raspberry Pi 5 through LiteRT-LM. LiteRT also supports GPU inference through a WebGPU/Vulkan backend, provides a CLI for model conversion, quantization, benchmarking, and inference, and offers access to optimized models through its Hugging Face community. The post concludes by noting planned support for Hailo AI accelerators, including Raspberry Pi AI HAT+ hardware.

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