TurboQuant sadly won't fix the RAM crisis, analysts say — here's why (www.techradar.com)

Google's recent announcement of TurboQuant, a compression algorithm aimed at reducing memory usage in AI, has sparked skepticism among analysts regarding its potential to mitigate the ongoing RAM crisis. While TurboQuant promises to cut key-value cache memory usage by a factor of six, enabling large language models (LLMs) to operate with significantly reduced memory consumption without sacrificing output quality, experts contend that this breakthrough might not lead to less demand for RAM in data centers. Instead, as AI models become more efficient, they could drive higher performance, leading to greater overall demand for memory to meet increased usage. Analysts, including those from Samsung Securities and Hana Securities, argue that optimization efforts tend to fuel AI performance advancements rather than ease semiconductor demand. Despite the technological promise of TurboQuant, its true impact remains uncertain, especially considering historical skepticism surrounding similar compression technologies. Current trends suggest that the RAM shortage will persist, with predictions for improvement in the supply-demand balance not expected until 2028. As the AI landscape continues to evolve, the competition for performance over efficiency means that memory consumption is likely to remain a crucial issue for the industry.