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, ...

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 ...

The vast proliferation and adoption of AI over the past decade has started to drive a shift in AI compute demand from training to inference. There is an increased push to put to use the large number ...

A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...

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 ...

AMD's stock remains highly volatile, reflecting ongoing uncertainty and rapid shifts in market sentiment, but still underperforms major indexes. Advanced Micro Devices is positioning itself for ...

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 ...