Can architecture choices improve inference efficiency without sacrificing accuracy?

Standard scaling laws (Chinchilla) optimize the trade-off between model parameters and training data for a fixed training compute budget. They say nothing about inference cost. But as LLMs move from research to deployment, inference cost dominates — and architecture choices affect inference efficiency in ways that parameter count alone does not predict.

The conditional scaling law augments Chinchilla by conditioning on three architectural variables: hidden size, the ratio of MLP parameters to attention parameters, and grouped-query attention (GQA) configuration. These variables affect inference throughput independently of their effect on accuracy. A model with the same parameter count and training budget can have dramatically different inference costs depending on how those parameters are allocated between MLP and attention layers.

Empirical validation across 200+ models (80M-3B parameters, 8B-100B training tokens): optimized architectures achieve up to 2.1% higher accuracy AND 42% greater inference throughput compared to LLaMA-3.2 under the same training budget. The "and" is the key finding — accuracy and inference efficiency are not zero-sum when architecture is treated as a free variable. Suboptimal architectures simultaneously sacrifice both.

This adds a third optimization lever to the inference compute landscape. Can inference compute replace scaling up model size? establishes the training-inference compute trade-off. Can we allocate inference compute based on prompt difficulty? establishes adaptive allocation. Architecture optimization sits upstream of both: it determines the baseline efficiency at which every unit of inference compute converts to performance. A 42% throughput improvement means the same inference budget produces 42% more reasoning attempts, parallel samples, or search steps.

For reasoning systems that scale inference compute extensively, the architectural multiplier compounds: a model that's 42% more efficient per inference step gets 42% more exploration per token budget, which matters disproportionately for approaches like Why does parallel reasoning outperform single chain thinking? where more parallel attempts directly improve accuracy.