Roman Chernin is the CBO and cofounder of AI infrastructure company Nebius. His career spans over 20 years in the tech industry. Every major advance in AI begins with model training, but the ...
Given the high costs and slow speed of training large language models (LLMs), there is an ongoing discussion about whether spending more compute cycles on inference can help improve the performance of ...
Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. Inferences, love them or hate them. You decide. One thing that ...
Predictive, generative, and agentic AI are not successive generations. They coexist inside enterprise systems, each placing fundamentally different demands on the underlying compute infrastructure.
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...
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 ...
Orbio, Surplus Intelligence, and Antseed are all making AI inference cheaper onchain, and each in a different way.
AMD is strategically positioned to dominate the rapidly growing AI inference market, which could be 10x larger than training by 2030. The MI300X's memory advantage and ROCm's ecosystem progress make ...
Qualcomm had datacenter envy back when Intel ruled the bit barns of the world, and now it has it even worse now that Nvidia has shown how AI processing can utterly transform the finances of a chip ...
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