Carmack Proposes Using Long Fiber Lines as L2 Cache for Streaming AI Data

John Carmack has proposed an innovative approach to AI memory architecture by suggesting the use of long fiber optic lines as L2 cache for streaming AI data, presenting a potential alternative to traditional DRAM solutions. This concept could address one of the most significant bottlenecks in local LLM deployment: memory bandwidth and capacity limitations that constrain model size and inference speed.

For local LLM practitioners, this represents a fascinating exploration of unconventional memory hierarchies that could enable running larger models on consumer hardware. While fiber-based memory caching might seem far-fetched, Carmack's track record in optimization and his deep understanding of computational constraints make this worth serious consideration. The approach could potentially allow for more efficient streaming of model weights and activations, reducing the memory pressure that currently limits local deployment of frontier models.

Read more about Carmack's fiber cache proposal on Tom's Hardware.

Source: Hacker News · Relevance: 8/10