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.
Seb Raschka maintains an excellent gallery of model architectures. You can use it to diff two of the best open models of their respective eras, Llama 3 and Nemotron 3 Ultra.
Attention might be all you need, but modern models certainly use a lot of different variants of it: query grouping, compressed, sparse, linear, sliding-window and more. Mixture-of-Experts added selective routing to feed-forward layers, and we have since started routing just about everything else too, from attention blocks to the residual stream. Vision and audio encoders have gone from bolted on to mixed-in, and models have scaled to run at inference time across multiple GPUs, which throws comms ops in that add extra boundaries in the middle of your model.
This is not too different from what happened with recsys. The basic architecture of recommendation systems, for the best part of a decade, was a relatively straightforward two-tower sparse neural net. The complexity came from the tension between the need to continually increase capabilities and the need to stay efficient, particularly for inference.
It’s tempting to assume that agents will Fix This: that you’ll hand your PyTorch or JAX definition to Claude Telenovela or whatever and have it generate optimally fused kernels 2 . To make that work you need a fixed, usable baseline to make sure that what is generated is… right.
What happened with recsys was that the gap between performance being an optimization and performance being a necessity became very, very small. Conceptually you can keep a pure model definition that gives you a baseline; in practice, training and testing a model takes significant resources and performance improvements become load-bearing.