AI everywhere is not a chatbot in every object. It means placing the smallest useful intelligence where the work happens. Our conclusion is local-first, not local-only: keep bounded tasks close to the device, escalate deliberately and measure the complete system.

I built a small inference engine to learn which optimizations help a 2B BitNet model on a 4 GB Raspberry Pi 5. geistlib reaches about 18 tokens/s and outpaces the tested bitnet.cpp baseline. The remaining measurements ask how close that result is to the hardware limit, what the model can answer and what its internal power telemetry can—and cannot—tell us.

BitNet b1.58 2B-4T generates 17.4 tokens per second on a Raspberry Pi 5 with the geisten runtime — roughly three times faster than a person reads. The setup, the numbers, and what still goes wrong.

Self-hosting an LLM in Europe is only the first step. A dependable system connects licensing, data protection, model provenance, runtime configuration and a signed deployment chain.

Speculative decoding pairs a fast draft model with a stronger verifier. The technique can reduce generation latency while preserving the output accepted by the target model.

TinyStories and Phi-3 show why compact language models matter. They make it possible to study inference, training objectives and deployment constraints without requiring frontier-scale infrastructure.

Ollama provides a simple way to run Llama 3 on a local machine. This short guide covers the minimal setup and the operational questions that matter after the first prompt.