Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in which the probabilities of tokens occurring in a specific order is ...

Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...

Google’s TurboQuant could cut LLM memory use sixfold, signaling a shift from brute-force scaling to efficiency and broader AI ...

A new compression technique from Google Research threatens to shrink the memory footprint of large AI models so dramatically ...

Abstract: The longest match strategy in LZ77, a major bottleneck in the compression process, is accelerated in enhanced algorithms such as LZ4 and ZSTD by using a hash table. However, it may results ...

Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...

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 ...

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. On March 24, 2026 Amir Zandieh and Vahab Mirrokni from Google Research published an article ...

[Digital Today Kyung-min Hong (홍경민), intern reporter] Google has unveiled TurboQuant, a new compression algorithm that can cut memory use and increase speed for large language models (LLMs). On March ...

Google published a research blog post on Tuesday about a new compression algorithm for AI models. Within hours, memory stocks were falling. Micron dropped 3 per cent, Western Digital lost 4.7 per cent ...