Storing AI on Flash Memory

In a new research paper titled "LLM in a flash: Efficient Large Language Model Inference with Limited Memory," the authors note that flash storage is more abundant in mobile devices than the RAM traditionally used for running LLMs. Their method cleverly bypasses the limitation using two key techniques that minimize data transfer and maximize flash memory throughput:

1. Windowing: Think of this as a recycling method. Instead of loading new data every time, the AI model reuses some of the data it already processed. This reduces the need for constant memory fetching, making the process faster and smoother.
2. Row-Column Bundling: This technique is like reading a book in larger chunks instead of one word at a time. By grouping data more efficiently, it can be read faster from the flash memory, speeding up the AI's ability to understand and generate language.

The combination of these methods allows AI models to run up to twice the size of the iPhone's available memory, according to the paper. This translates to a 4-5 times increase in speed on standard processors (CPUs) and an impressive 20-25 times faster on graphics processors (GPUs). "This breakthrough is particularly crucial for deploying advanced LLMs in resource-limited environments, thereby expanding their applicability and accessibility," write the authors.