Portfolio optimization benchmarks often collapse into one headline number: speedup. That number is easy to remember and easy to misuse. A solver can be fast on a toy problem, fast after expensive preparation, or fast while producing a portfolio that misses the business constraints.

For institutional finance, the benchmark has to answer a harder question: can the engine solve the real workload, with real constraints, fast enough to change the operating model?

Figure 1: What a useful benchmark separates

Cold startMeasures the cost when nothing useful is warm.
Repeated runMeasures the production pattern after the workflow is active.
Quality gateReports gap, violations, and pass/fail threshold.
Matched loadUses the buyer's universe, risk model, and constraints.

Cold-start and repeated-run evidence answer different questions

A cold-start run asks how the system behaves when it has to initialize. A repeated-run benchmark asks what happens when a recurring production workflow solves the same structure many times. Both matter, but mixing them creates false confidence.

PRISM separates these surfaces. Public evidence includes corrected cold-start timing on a smaller real-data universe and repeated-run transition workflows at 5,000 assets. The point is not to force one number to explain everything. The point is to show where GPU-native execution changes the practical workflow.

Figure 2: Benchmark signals to read together

Runtime
Necessary
Quality gap
Critical
Failure rate
Operational
Audit trail
Required

Quality gates matter as much as runtime

A fast answer with unacceptable tracking error, constraint drift, or tax behavior is not a win. Benchmark tables should report the quality gap against the reference method and state the pass/fail threshold before the result is interpreted.

This is where PRISM's positioning is intentionally narrow: GPU-native speed is useful only when it stays inside production-quality bands. The stack is designed around routed execution, independent quality verification against a reference optimum, replayable outputs, and explicit quality comparison rather than speed-only claims.

Use matched workloads for final judgment

Public benchmarks establish credibility. They do not replace your own workload. The strongest evaluation uses your universe, your constraints, your risk model, your trading rules, and your current baseline. That is the point of a matched-workload pilot.

Practical next step: define one representative daily rebalance, one transition scenario, and one stress case. Measure runtime, quality gap, failure rate, and audit artifacts for each.