A.I. chip, Maia 200, calling it “the most efficient inference system” the company has ever built. The Satya Nadella -led tech ...

The next generation of inference platforms must evolve to address all three layers. The goal is not only to serve models ...

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, ...

As AI workloads move from centralised training to distributed inference, the industry’s fibre infra challenge is changing ...

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 ...

AI inference uses trained data to enable models to make deductions and decisions. Effective AI inference results in quicker and more accurate model responses. Evaluating AI inference focuses on speed, ...

The major cloud builders and their hyperscaler brethren – in many cases, one company acts like both a cloud and a hyperscaler – have made their technology choices when it comes to deploying AI ...

A decade ago, when traditional machine learning techniques were first being commercialized, training was incredibly hard and expensive, but because models were relatively small, inference – running ...

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