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

The inference era is not here yet at full scale. But the infrastructure decisions made today will determine who is ...

The simplest definition is that training is about learning something, and inference is applying what has been learned to make predictions, generate answers and create original content. However, ...

The centralized mega-cluster narrative is seductive – but physics, community resistance, and enterprise pragmatism are ...

KubeCon Europe 2026 made AI inference its central focus with major CNCF donations including llm-d, Nvidia's GPU DRA driver ...

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

Just when investors may have gotten a firm grasp on artificial intelligence (AI), the game is changing again. According to Deloitte Global's TMT Predictions 2026 report, inference will account for two ...

The practical implication is that sovereign AI infrastructure built today should prioritise inference throughput, not just ...

Nvidia is reportedly developing a specialized processor aimed at accelerating AI inference, a move that could reshape how companies like OpenAI deploy their models. The push comes as Nvidia has also ...

Inference is typically faster and more lightweight than training. It's used in real-time applications like chatbots, recommendation engines, voice recognition, and edge devices like smartphones or ...