Large-scale applications, such as generative AI, recommendation systems, big data, and HPC systems, require large-capacity ...

Lyra 2.0 is Nvidia’s framework for generating persistent, explorable 3D scenes from one image. Here is what it is, how it ...

The iDX6011 Pro impresses with an easy setup and all the standard NAS options you’d usually expect from a mid-range NAS. The ...

Adarsh Mittal, a senior application-specific integrated circuit engineer, explores why many memory performance optimizations ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the ...

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

At 100 billion lookups/year, a server tied to Elasticache would spend more than 390 days of time in wasted cache time.

Die-to-die chiplet standards are only the beginning. Many more standards are necessary for a chiplet marketplace. A number of such standards have either had initial versions released or are in ...