modelcomputecoststate

ModelComputeCostState (often written as modelcomputecoststate) is a data construct used in machine learning and model serving systems to capture the estimated computational cost of executing a model under a given configuration. It provides a concise, normalized snapshot of resource demands that can be consulted by schedulers, autorscalers, and deployment managers to make cost-aware decisions without executing the model repeatedly.

Typically, the state records metrics such as estimated latency, peak memory usage, energy consumption, and operational cost, together with contextual fields like the hardware profile, batch size, input size, and precision mode. The exact fields vary by system, but the goal is a consistent interface to compare competing models or configurations.

Construction and maintenance: modelcoststate values are derived from profiling runs, microbenchmarks, historical telemetry, and simple cost models that map resource usage to time and price. They can be static (calibrated at deployment) or updated online as workloads evolve, using regression models, moving averages, or Bayesian updates to reflect recent performance.

Usage: service platforms use the state to route queries, select models, or adjust autoscaling to meet latency SLAs while controlling cost. It also supports budgeting, experimentation, and model selection across different hardware backends or precision settings.

Limitations and considerations: cost estimates may become stale if workloads shift or hardware changes, and multi-tenant environments can introduce variance. The usefulness of modelcomputecoststate depends on accurate profiling and appropriate aggregation across concurrent requests. Related concepts include compute cost models, profiling, autoscaling policies, and energy-aware scheduling.