Sales of Intel's central processing units and custom AI processors are gaining traction as AI inference workloads grow.

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...

As frontier models move into production, they're running up against major barriers like power caps, inference latency, and rising token-level costs, exposing the limits of traditional scale-first ...

Inference workloads are on course to consume a significant chunk of AI computing power in 2026. Intel is well positioned to capitalize on the growing demand for AI inference thanks to the efficiency ...