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 the AI industry moves toward 2026, its center of gravity is undergoing a decisive shift. Nvidia’s effective absorption of xAI’s large language model, Grok, symbolizes a bro ...

There’s a lot of hyperbole around artificial intelligence these days. However, there are a lot of good intentions as well, and many are looking to build AI that doesn’t involve haves and have-nots.

Processor hardware for machine learning is in their early stages but it already taking different paths. And that mainly has to do with dichotomy between training and inference. Not only do these two ...

AMD (AMD) is rated a 'Buy' based on its architectural strengths and plausible 3-5 year EPS growth framework. AMD’s higher ...

Earlier in May during The Next AI Platform event in San Jose, we conducted live, technical interviews with a broad range of experts in various areas of deep learning hardware. This included the ...

AI/ML can be thought about in two distinct and essential functions: training and inference. Both are vulnerable to different types of security attacks and this blog will look at some of the ways in ...

With Groq Cloud continuing and key staff moving to NVIDIA, the $20B license promises lower latency and simpler developer ...

Artificial intelligence has many uses in daily life. From personalized shopping suggestions to voice assistants and real-time fraud detection, AI is working behind the scenes to make experiences ...

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 ...