Inference Under Pressure: When Scaling Laws Meet Real-World Constraints
Budget. Not the inspirational kind that appears in founder decks as “disciplined growth.” The real kind: GPU invoices, latency targets, queueing delays, memory ceilings, unhappy users, and the quiet discovery that a model can be brilliant in a benchmark and still economically annoying in production. That is the useful tension behind Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs.1 The paper does not merely repeat the familiar lesson that large language models become expensive when they get larger. Everyone with a cloud bill has already enjoyed that seminar. Its sharper point is that the usual scaling-law conversation leaves out a design variable that businesses eventually pay for: architecture. ...