LLM System Design Interview, issue 28, Apr 18, 2026
The Memory-Bound Decoding Trap
Senior AI Engineer interview at Anthropic, and the interviewer asks:
“Your autoregressive decoding is completely memory-bound, making token generation painfully slow. Instead of trying to optimize the memory bandwidth directly, how can you leverage a significantly smaller, ‘weaker’ model to artificially parallelize the generation process of your massive frontier model?”
Don’t say: “We should build a routing layer to send easy prompts to the small model and hard prompts to the big one.”
When scaling models stops speeding you up, and the counterintuitive trick that uses a smaller model to break the bottleneck.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.