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

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 ...

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 ...

A food fight erupted at the AI HW Summit earlier this year, where three companies all claimed to offer the fastest AI processing. All were faster than GPUs. Now Cerebras has claimed insanely fast AI ...

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 ...

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 ...

These tech stocks look particularly well positioned to benefit from this opportunity.

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 ...

Ahead of Nvidia Corp.’s GTC 2026 this week, we reiterate our thesis that the center of gravity in artificial intelligence is shifting from “How fast can you train?” to “How well can you serve?” ...

To understand what's really happening, we need to look at the full system, specifically total cost of ownership of an AI ...

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