This article was originally published at The Conversation. The publication contributed the article to Space.com's Expert Voices: Op-Ed & Insights. Professional astronomers don’t make discoveries by ...

Abstract: Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery ...

Imaging is a critical technique in biology—from identifying cancerous cells in biopsies to observing how immune cells like macrophages hunt down and destroy pathogens. Traditionally, distinguishing ...

Google’s TurboQuant is making waves in the AI hardware sector by addressing long-standing challenges in memory usage and processing efficiency. Developed with components like the Quantized ...

TurboQuant (arXiv 2504.19874, ICLR 2026) compresses the key-value cache that transformer models maintain during inference. It does not touch model weights. Its purpose is to reduce memory consumption ...

Memory prices are falling, and stock prices of memory companies took a hit, following news from Google Research of a breakthrough that will greatly reduce the amount of memory needed for AI processing ...

We have seen the future of AI via Large Language Models. And it's smaller than you think. That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way ...

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” ...

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 ...