The focus of artificial-intelligence spending has gone from training models to using them. Here’s how to understand the ...

AI inference uses trained data to enable models to make deductions and decisions. Effective AI inference results in quicker and more accurate model responses. Evaluating AI inference focuses on speed, ...

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 message from Nvidia is that AI is no longer about models or chips, but about monetizing inference at scale – where tokens ...

Roman Chernin is the CBO and cofounder of AI infrastructure company Nebius. His career spans over 20 years in the tech industry. Every major advance in AI begins with model training, but the ...

Interactive LLMs (chat, copilots, agents) with strict latency targets Long‑context reasoning (codebases, research, video) with massive KV (key value) cache footprints Ranking and recommendation models ...

Until recently, most AI was in data centers/cloud and most of that was training. Things are changing quickly. Projections are AI sales will grow rapidly to tens of billions of dollars by the mid 2020s ...