Here's Why 100TB+ SSDs Will Play A Huge Role In Ultra Large Language Models In The Near Future
techradar, Monday, January 20th, 2025
Kioxia's AiSAQ solution uses high capacity drives to handle large datasets
Large language models often generate plausible but factually incorrect outputs - in other words, they make stuff up. These "hallucination"s can damage reliability in information-critical tasks such as medical diagnosis, legal analysis, financial reporting, and scientific research.
Retrieval-Augmented Generation (RAG) mitigates this issue by integrating external data sources, allowing LLMs to access real-time information during generation, reducing errors, and, by grounding outputs in current data, improving contextual accuracy. Implementing RAG effectively requires substantial memory and storage resources, and this is particularly true for large-scale vector data and indices. Traditionally, this data has been stored in DRAM, which, while fast, is both expensive and limited in capacity.