Traditional graphics rendering could be in the rearview mirror soon enough ...

Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches ...

A research team has developed a Gaussian Splatting processing platform that supports end-to-end processing from data acquisition to multi-platform rendering. Their framework provides a solid ...

It works like magic, but won't renew your old 8GB card's lease on life ...

Researchers have developed a dynamic range compression dual-domain attention network for enhancing tunnel images under extreme exposure conditions, a problem that continues to challenge transportation ...

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

To improve data center efficiency, multiple storage devices are often pooled together over a network so many applications can share them. But even with pooling, significant device capacity remains ...

Intel TSNC brings neural texture compression with up to 18x reduction, faster decoding, and flexible SDK support for modern ...

Abstract: The widespread deployment of phasor measurement units (PMUs) has introduced unprecedented challenges in handling the transmission and storage of extensive synchrophasor data. Addressing ...

Running a 70-billion-parameter large language model for 512 concurrent users can consume 512 GB of cache memory alone, nearly four times the memory needed for the model weights themselves. Google on ...

Add Decrypt as your preferred source to see more of our stories on Google. Google said its TurboQuant algorithm can cut a major AI memory bottleneck by at least sixfold with no accuracy loss during ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...