This article explores the efficiency of local AI models compared to centralized cloud infrastructure. It introduces a metric called intelligence per watt (IPW) to evaluate local models' performance and energy use. The findings indicate that local models can accurately handle a significant portion of queries, and they outperform cloud models in terms of efficiency.

This article explores the complexities of LLM inference, focusing on the two phases: prefill and decode. It discusses key metrics like Time to First Token, Time per Output Token, and End-to-End Latency, highlighting how hardware-software co-design impacts performance and cost efficiency.