Learn the right VRAM for coding models, why an RTX 5090 is optional, and how to cut context cost with K-cache quantization.

As AI becomes more like a recurring utility expense, IT decision-makers need to keep an eye on enterprise spending. The costs of GPU use in data centers could track with overall costs for AI.

What if you could harness the power of innovative artificial intelligence without relying on the cloud? Imagine running a large language model (LLM) locally on your own hardware, delivering ...

AI hyperscaler startup Nscale has signed a sizable deal with Microsoft to bring Nvidia AI hardware to multiple data centers. The AI cloud provider announced on Wednesday that it signed a deal with ...

The software relies on a customer-managed agent installed within each environment. That agent collects detailed system data and sends it to a centralized dashboard hosted on ...

A new class of AI service clouds—GPU clouds and so-called neoclouds—have stormed the market and drawn investor interest. Before we get too excited, however, we should take a close look at how these ...

For the last few years, the term “AI PC” has basically meant little more than “a lightweight portable laptop with a neural processing unit (NPU).” Today, two years after the glitzy launch of NPUs with ...

How CPU-based embedding, unified memory, and local retrieval workflows come together to enable responsive, private RAG ...