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 ...

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 ...

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 ...