AI computing demand shifts from model training to inference

The landscape of artificial intelligence computing is shifting from model training to inference. While the early stages of the AI boom focused on the massive computational expense of training models used by companies like OpenAI, Google, and Anthropic, the industry is now moving toward putting that intelligence to work.

According to Deloitte, inference—the process of using a trained model to answer queries or perform tasks—is becoming the primary driver of AI computing demand. In 2023, inference accounted for approximately one-third of all AI computing power; this figure reached roughly half last year and is expected to climb to two-thirds in 2024. This shift creates significant incentives for developers to make AI faster and more cost-effective.

In parallel with these infrastructure shifts, technical frameworks like Spring AI are evolving to handle complex retrieval tasks. New modular RAG (Retrieval-Augmented Generation) pipelines, such as those utilizing the Jev model, aim to improve the accuracy of AI responses by filtering and reranking retrieved data to ensure only relevant, high-quality information reaches the prompt.