This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...

Key market opportunities for AI inference chips include the rise of AI model deployment across industries, demand for ...

Thanks to “data inference” technology, companies know more about you than you disclose. By Zeynep Tufekci Dr. Tufekci is a professor of information science who specializes in the social effects of ...

Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed ...

Training gets the hype, but inferencing is where AI actually works — and the choices you make there can make or break real-world deployments. Inferencing is an important part of how the AI sausage is ...

Google researchers have warned that large language model (LLM) inference is hitting a wall amid fundamental problems with memory and networking problems, not compute. In a paper authored by ...

While the tech world obsesses over headlines about the $100 million price tag to train GPT-4, the real economic story is happening in inference: the ongoing cost of actually running AI models in ...