Raspberry Pi 5 16GB RAM as the central brain for an autonomous disaster recovery vehicle
To make this complex system actually work in a life-or-death disaster scenario, you must strategically optimize your software stack and offload specific tasks. 1. The AI Stack: Swapping vLLM for llama.cpp / Ollama The Problem with vLLM: While vLLM is an incredible inference engine, it is heavily optimized for high-throughput enterprise server GPUs and relies on massive memory allocations (vLLM PagedAttention). Running vLLM on an ARM CPU like the Raspberry Pi 5 is incredibly inefficient and will consume all your RAM and CPU cores just keeping the model alive. [1, 2, 3] The Solution: Use llama.cpp or Ollama . They use highly-optimized CPU quantization (4-bit or 5-bit GGUF formats). [4, 5, 6] Model Selection: Run Gemma-2-2B-IT (Instruction Tuned) or Gemma-2B-Vision quantized to 4-bits ( Q4_K_M ). This model will only take up about 1.6 GB to 2.2 GB of your 16GB RAM, leaving plenty of room for your sensors and SLAM algorithms. [5, 6] Expected Speed: You will get roughly 5 to 7 to...