Building the Multivariate Inference Efficiency Model
DDN, Monday, August 3rd, 2026
A framework combining workload, system, hardware, and cost variables to predict AI inference performance under production traffic.
DDN presents a multivariate model for predicting AI inference efficiency that moves beyond simple tokens-per-second metrics.
The model incorporates workload variables such as hidden reasoning tokens and context reuse, system factors including cache hit rates and disaggregation overhead, hardware constraints, and economic costs.
Key equations establish effective cost per successful answer and retrieve-versus-recompute breakeven points, showing that high-performance storage directly affects tiered cache performance. The framework lets infrastructure teams parameterize real production traces and run sensitivity analysis for GPU pool sizing and cache policy decisions.