Large language models (LLMs) have revolutionized NLP, but their massive size makes fine-tuning for specific tasks a challenge. Existing methods like LoRA (Low-Rank Adaptation) help, but still require significant storage, especially when deploying numerous personalized models. Imagine serving a million custom AI assistants—the memory demands become enormous. Now, researchers have introduced LoRA-XS (Low-Rank Adaptation with eXtremely Small number of parameters), a groundbreaking technique that slashes the number of trainable parameters without sacrificing performance. LoRA-XS achieves this incredible efficiency by cleverly inserting a tiny, trainable matrix between frozen, low-rank matrices derived from the original model's weights. This allows for fine-tuning with drastically reduced storage needs, opening doors to deploying millions of personalized models with minimal memory overhead. In tests across various benchmarks like GLUE, GSM8K, MATH, and commonsense reasoning datasets, LoRA-XS performs on par or better than LoRA and other cutting-edge methods like VeRA, all while being dramatically more parameter-efficient. For instance, in 7B models, LoRA-XS reduces trainable parameters by over 100x compared to LoRA. This means that serving a million personalized GPT-3 models with LoRA-XS would require only gigabytes of storage, compared to *terabytes* with standard LoRA—a difference of over 1500x! This breakthrough has significant implications for the future of LLMs. It enables broader access to personalized AI, allowing developers and companies to create custom models for specific user needs without massive infrastructure investments. It also paves the way for more efficient on-device deployment of LLMs, potentially bringing powerful AI capabilities to smartphones and other resource-constrained devices. While challenges remain in scaling these techniques to even larger models, LoRA-XS represents a crucial step towards making LLMs more accessible, adaptable, and sustainable.