LLM System Design Interview, issue 26, Apr 16, 2026
The Attention Optimization Trap
Senior AI Engineer interview at OpenAI, and the interviewer asks:
“You spent months optimizing the attention layers on our 1.4B model and got a massive speedup. Why will that exact same profiling strategy completely fail to move the needle when we scale that architecture to 175B parameters?”
Don’t say: “I would just implement FlashAttention-2, quantize the KV cache, and use Ring Attention to handle the quadratic sequence length bottleneck.”
Why obsessing over attention kernels at scale quietly wastes months, while the real 90% FLOP sink goes untouched.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.