GPSUSA sits at the intersection of Systems Engineering and Financial Quant Theory, treating GPUs as governed capital — priced, allocated, and enforced by policy.
We build economic control planes that convert GPU behavior and AI workloads into programmable business constraints.
We replace utilization vanity metrics with real unit economics — enforcing cost, fairness, and performance at runtime.
tokens/sec/$ by model, tenant, and workload class — tied to cost attribution you can defend.
Fairness, routing, isolation, and cost envelopes enforced by a control plane — not tribal process.
Reduce drift and regressions through measurable guardrails and continuous economic optimization.
Unit economics, fairness, leakage, and p99 stability — shown as CFO-defensible metrics (mock telemetry).
Structural waste signal over time (mock).
Tail latency tightening under policy (mock).
Activate token, GPU-second, and workload telemetry to generate a fleet-wide economic baseline.
Enable fairness, routing, and cost guardrails across controlled production slices.
Expand runtime governance across inference + training surfaces with deterministic p99 controls.
Continuously tune runtime + scheduling using live economic + performance signals.
