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

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 ...

The vast proliferation and adoption of AI over the past decade has started to drive a shift in AI compute demand from training to inference. There is an increased push to put to use the large number ...

If the hyperscalers are masters of anything, it is driving scale up and driving costs down so that a new type of information technology can be cheap enough so it can be widely deployed. The ...

Gimlet Labs raises $80M in Series A funding to tackle the AI inference bottleneck with a new multi-silicon cloud platform.