INFRA Signal 472
Mastering Edge AI on Raspberry Pi with LiteRT and Gemma
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Google's LiteRT runtime and Gemma open models now enable real-time local LLM inference on Raspberry Pi 5, with Gemma 4 E2B achieving 9 tokens/sec decode at 1432 MB peak memory.
Engineers building robotics or edge systems can run capable language models entirely offline on commodity ARM hardware, removing cloud dependencies and network latency. The demonstrated throughput (~300 wpm) exceeds normal human speech speed, making local autonomous agents and real-time voice interaction practical on a low-cost board.
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LiteRT-LM orchestrates Gemma deployment on Raspberry Pi with CPU acceleration via XNNPACK, achieving 99 tokens/sec prefill and 9 tokens/sec decode for Gemma 4 E2B.
The Gemma family spans from 270M parameter models for narrow tasks to E4B models for complex multi-step planning, each targeting different edge memory and latency constraints.
GPU execution on Raspberry Pi 5's Broadcom VideoCore VI is supported through LiteRT, extending inference performance beyond CPU-only paths.
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