AI inference is becoming a continuous operating cost, and the next efficiency gains will not come only from bigger chips, larger clusters, or better utilization dashboards. They will also come from better runtime control.
Hardware defines the available ceiling, but production behavior determines how much useful work an existing GPU fleet actually delivers. Runtime control provides a way to examine that operating layer rather than treating utilization as the complete answer.
Vectris is focused on helping infrastructure teams identify, govern, and certify recoverable capacity inside existing GPU fleets. That work centers on disciplined baseline-versus-controlled testing, quality gates, overhead accounting, hardware-counter review, and evidence that technical teams can inspect.
What infrastructure teams need to evaluate
- Cost per token and watts per token
- Memory traffic and useful throughput
- Recoverable capacity and the evidence required to credit it