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

We all have the habit of trying to guess the killer in a movie before the big reveal. That’s us making inferences. It’s what happens when your brain connects the dots without being told everything ...

Ahead of Nvidia Corp.’s GTC 2026 this week, we reiterate our thesis that the center of gravity in artificial intelligence is ...

Edge AI is the physical nexus with the real world. It runs in real time, often on tight power and size budgets. Connectivity becomes increasingly important as we start to see more autonomous systems ...

Nvidia just paid $20 billion for Groq's inference technology in what is the semiconductor giant's largest deal ever. The question is: Why would the company that already dominates AI training pay this ...

Startups as well as traditional rivals are pitching more inference-friendly chips as Nvidia focuses on meeting the huge demand from bigger tech companies for its higher-end hardware. But the same ...

You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs. We’re years into the ...

AWS partnered with Cerebras. Microsoft licensed Fireworks. Google built Ironwood. One week of announcements reveals who ...

The edge inference conversation has been dominated by latency. Read any survey paper, attend any infrastructure conference, and the opening argument is nearly always the same: cloud inference ...

Despite ongoing speculation around an investment bubble that may be set to burst, artificial intelligence (AI) technology is here to stay. And while an over-inflated market may exist at the level of ...