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’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises ...

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

Abstract: A novel direct method for electromagnetic scattering analysis is introduced by enhancing the principal component analysis (PCA) compression algorithm with the multilevel fast multipole ...