Operations | Monitoring | ITSM | DevOps | Cloud

For the past few years, the narrative around Artificial Intelligence has been dominated by what I like to call the "magic box" illusion. We assumed that deploying AI simply meant passing a user’s question through an API key to a Large Language Model (LLM) and waiting for a brilliant answer.

The wrong people got the most attention from Jensen Huang’s comments last week. Huang told the All-In Podcast that he’d be “deeply alarmed” if a $500,000 engineer consumed less than $250,000 in AI tokens annually. Within 48 hours, the discourse collapsed into a compensation debate.

The best QA engineers are always asking themselves (and others around them) what might break. When engineering teams shifted to agile delivery, that mindset largely moved out of dedicated roles and into the background. Automated testing took over the repetitive work, developers owned quality end-to-end, and velocity improved. What didn't carry over was the habit of looking at a feature and asking how a real user, an edge case, or unexpected load might expose it.

Since the advent of Artificial Intelligence (AI), it has become the buzzword for modern day businesses. It has tremendous benefits which has lured enterprises invest hefty money with a view of getting ahead of their competitors. Yet, many CIOs are still figuring out ways to get the best ROI of AI that resonates with their businesses. While there are many initial programs and proof of concepts that show promise, in the long run they fail to deliver their promise.

While GPUs remain among NVIDIA's flagship products, they also offer a range of other compute products beyond the dedicated graphics cards for which they are known. If you are unfamiliar with the words DGX or HGX, this blog is for you. Throughout this blog, we will cover what these terms mean in practice and when you should be using them.