LLMs Are Complicated Now
This article explores the increasing architectural complexity of modern Large Language Models, comparing their evolution to the historical trajectory of recommendation systems. It highlights how the shift toward specialized components like Mixture-of-Experts and multi-GPU inference is creating new challenges for model optimization and maintenance.
Why it matters
As AI models become more complex, the industry faces a growing gap between theoretical model definitions and the practical, resource-intensive reality of deploying them at scale.
Back in 2022 and 2023 there were two big branches of machine learning happening at Meta 1 . The LLM work that led to Llama was a clean, smooth stack of repeated Transformer modules; the recommendation systems graphs were, by contrast, terrifying. Luckily, the industry has remedied that state of affairs by making LLMs a lot more complicated.
The article provides a technical analysis of industry trends without taking a political or ideological stance.
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