AI BriefingLLMs Have Become Complex Now
Key point
As LLM architectures grow more sophisticated, a shift in design approach is needed to secure both performance optimization and research flexibility at the same time.
Details
In the past, early LLMs such as Llama had a simple structure composed of repeated Transformer modules, but modern models have taken on a highly complex form.
The latest models use various variants of the Attention mechanism (Query grouping, Sparse, Sliding-window, etc.), and with Mixture-of-Experts(MoE), routing has reached a stage where not only feedforward layers but also attention blocks and the residual stream are routed. In addition, vision and audio encoders have been integrated into models, and communication operations for multi-GPU inference have been added, maximizing structural complexity.
This complexity is similar to the problems that recommendation systems(Recsys) faced in the past. As performance optimization becomes not merely optional but essential, improving a model's performance is directly tied to the system's survival.
Therefore, to increase the iteration speed of research, rather than simply relying on agents that generate optimized kernels, Composability must be considered from the design stage. Having a structure that allows flexible experimentation with various operations while minimizing performance degradation, like PyTorch's FlexAttention, is a core challenge of modern AI research.
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