Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

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

Google just sucker-punched these highflying tech stocks — don’t let the relief rally fool you

Google’s TurboQuant cracks the memory-chip cartel — and the hardware-heavy AI thesis now looks like yesterday’s news.
IEEE

Robustness of Machine Learning Based Compression

Abstract: We compare in this work the robustness of machine learning based image compression algorithms with classical algorithms such as JPEG. For this, we run adversarial attacks against [2] and [1] ...
The Next Web

Google’s new compression algorithm cut memory stocks within hours of publication

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 ...
Ars Technica

Google’s TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...
IEEE

Design and Implementation of a Multi-Algorithm Lossless Compression Framework for Hyperspectral Images on FPGA

Abstract: This paper presents a hardware-optimized framework for lossless hyperspectral image compression using multiple algorithms implemented on Field-Programmable Gate Arrays (FPGAs). The framework ...