A benchmark score is the result of a particular assessment under particular conditions. To make a useful comparison, understand what was measured and which conditions stayed the same.
The first question is simple: does this benchmark resemble the decision you need to make? A faster result on one workload may say little about another.
Match the workload and version
Check the task, input distribution, dataset size, and benchmark release. If a specification changes, understand how that affects the interpretation of its metrics before comparing old and new results.
TPC-H documents a change to its price/performance multiplier between Versions 2 and 3. Comparing numbers without that context can give the wrong impression even when the underlying results are valid.
Understand the measurement rules
Identify what the metric rewards. Throughput, latency, accuracy, cost, and energy answer different questions. Read the rules for how each is measured, and check whether higher or lower values are desirable.
For MLPerf Inference: Datacenter, the scenario and metric rules are part of the benchmark. Use the relevant scenario, workload, version, and division when choosing comparable results.
Keep the configuration visible
Hardware, software, tuning, and operating conditions belong with the result. They help explain both the performance you see and the effort required to reproduce it.
Decide which configuration differences are relevant to your decision. If you are choosing an entire system, those differences may be the point. If you are isolating a component, they may confound the comparison.
Use the result as evidence
A benchmark supports a claim within its scope. It does not substitute for testing the conditions that matter to your own work. Keep a short note explaining why the benchmark is relevant and which questions it leaves open.
Our catalog’s comparison view brings these contextual details together. For actual performance comparisons, return to the publisher’s rules and complete results.