This week’s semiconductor developments highlight how AI’s hardware backbone is evolving from all sides, from high-bandwidth memory to cloud-scale infrastructure. Samsung’s unveiling of its next-generation HBM4 chips signals a renewed …

The memory wall is more of an issue than ever in AI workloads. How will it be fixed? As AI workloads scale, compute performance is increasing far faster than memory …

Is one kind of AI processor likely to reign supreme? GPUs have long been the workhorse behind much of the AI infrastructure buildup. But as AI needs have grown, specialized …

Edge AI silicon is getting much more capable, but how much will edge AI really handle? Artificial intelligence is moving outside massive data centers. As silicon evolves, AI processing is …

The Nvidia DGX Spark is a so-called “supercomputer” built for AI — but what does that mean, and why would you want one?

Semiconductor packaging is rapidly advancing, but how will it impact AI?

There might be movement from vendors like Nvidia and AMD, but supply issues run a whole lot deeper A few short years ago, it would have been hard to predict …

Custom inference chips could help drive down soaring costs — but Nvidia isn’t going anywhere soon While AMD and Nvidia battle for AI accelerator supremacy, hyperscalers and other major players …

AMD’s 2nm leap is real, but Nvidia’s dominance runs deeper than silicon In sum – what to know: AMD jumps to 2 nm first – The Instinct MI450 will be …

Today’s top stories focus predominantly on the big players: OpenAI, AMD, Nvidia, et al. They highlight a major trend in AI semiconductors – Nvidia is the reigning champ right now, …