Building a Systemic Autonomy Agent: OpenClaw + Gemma 4 & TurboQuant on Raspberry Pi 4B

Short summary

This detailed tutorial demonstrates building autonomous AI agents on edge hardware (Raspberry Pi 4B) with Gemma 4 optimized using TurboQuant KV cache compression. It covers hardware setup with SSD booting and thermal management, compiling llama.cpp with ARM NEON acceleration, and memory optimization strategies. The guide includes complete step-by-step commands and configuration for long-context inference on constrained hardware.

• •Deploy Gemma 4 LLM on Raspberry Pi 4B with TurboQuant compression for autonomous agents
• •Hardware setup: SSD booting, thermal management, memory optimization for 8GB RAM constraints
• •Compilation guide for llama.cpp with ARM NEON SIMD acceleration and KV cache compression

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