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

More investors need to hear of and learn about ASML.

Nvidia's Groq 3 LPU chip widens the AI gap with China, but offers Chinese firms niche inference market opportunities, analysts say Nvidia's latest language processing chip, unveiled at the company's annual artificial intelligence conference,

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?” Training has ushered in the modern AI era.

Amazon Web Services says the partnership will allow it to offer lightning-fast inference computing.

Artificial intelligence has to "reason" and "think," meaning that "the inflection point of inference has arrived." "It's way past training now," he added. While Nvidia chips were once heavily used to train AI models,

Lightbits Labs Ltd. today is introducing a new architecture aimed at addressing one of the most stubborn bottlenecks in large-scale artificial intelligence inference: the growing mismatch between the memory demands of large language models and the limited memory capacity of graphics processing units.

Amazon and Cerebras launch a disaggregated AI inference solution on AWS Bedrock, boosting inference speed 10x.