LLMs are complicated now – Ian’s Blog

Modern LLMs have become architecturally complex with numerous attention variants (query grouping, sparse, linear, sliding-window), mixture-of-experts routing, vision/audio encoders, and multi-GPU inference requirements—mirroring the evolution of recommendation systems where the tension between capability and efficiency drove increasing complexity. Effective research iteration on these systems requires designing for composability from the start rather than hand-fusing optimizations, enabling fast exploration without sacrificing performance; tools like PyTorch's FlexAttention demonstrate this principle by allowing kernel generation for a class of operations while maintaining verifiability and only mild performance overhead.

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