You doubled the GPUs but training barely got faster. Why doesn't distributed training scale linearly?
Linear scaling is the marketing figure; the actual curve bends early for reasons rooted in physics, not bugs. Here is where the speedup leaks and how to recover it.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Linear scaling is the marketing figure; the actual curve bends early for reasons rooted in physics, not bugs. Here is where the speedup leaks and how to recover it.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
