Yes, this from the paper:

> "The key insight motivating LtRAM is that long data lifetimes and read heavy access patterns allow optimizations that are unsuitable for general purpose memories. Primary applications include model weights in ML inference, code pages, hot instruction paths, and relatively static data pages—workloads that can tolerate higher write costs in exchange for lower read energy and improved cost per bit. This specialization addresses fundamental mismatches in current systems where read intensive data competes for the same resources as frequently modified data."

Essentially I guess they're calling for more specific hardware for LLM tasks, much like was done with all the networking equipment for dedicated packet processing with specialized SRAM/DRAM/TCAM tiers to keep latency to a minimum.

While there's an obvious need for this for traffic flow across the internet, whether or not LLMs are really going to scale like that, or there's a massive AI/LLM bubble about to pop, would be the practical issue, and who knows? The tea leaves are unclear.