Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory costs and time-to-first-token by up to 8x for multi-turn AI applications.

The latest GPT-5.4 mini model delivers benchmark results surprisingly close to the full GPT-5.4 model while running much faster, signaling a shift toward smaller AI models powering real-world ...

In large retail operations, category management teams spend significant time deciding which product goes onto which shelf and in which order. Shelf space is very expensive real estate in retail.

Electrical distribution systems are characterized by dynamic operating conditions and complex network topologies, which pose significant challenges for the effective deployment of protection schemes.

Where do AI systems lose confidence in your content? Discovery, selection, crawling, rendering, and indexing hold the answer.

Abstract: Optimization algorithms are widely employed to tackle complex problems, but designing them manually is often labor-intensive and requires significant expertise. Global placement is a ...

When we talk about the cost of AI infrastructure, the focus is usually on Nvidia and GPUs — but memory is an increasingly important part of the picture. As hyperscalers prepare to build out billions ...

Researchers at Nvidia have developed a technique that can reduce the memory costs of large language model reasoning by up to eight times. Their technique, called dynamic memory sparsification (DMS), ...

According to Jeff Dean on Twitter, concrete examples of various AI performance optimization techniques have been provided, including high-level descriptions from a 2001 set of changes. These examples ...

Each year when MD+DI editors sit down to discuss Medtech Company of the Year prospects, the companies that rise to the top for us tend to be those that have had a transformational year either through ...

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