NIM benchmarking guidance keeps throughput separate from application quality

Start with a service that becomes slow or unreliable as people use it. Include business-process owners, frontline staff, IT and finance. Ask which tasks must finish promptly, how much context they require and whether users spend time correcting responses. A bounded evaluation could compare the existing workflow with a limited assistant using agreed tasks and demand conditions. The value hypothesis is acceptable task completion at a sustainable operating cost. Treat the guide as a way to structure evidence requests. Do not convert token throughput into staff savings, accuracy or a claim that autonomous agents will improve the service.

Create a reproducible workload manifest covering model version, input and output distributions, concurrency, sampling and hardware. Test a stable endpoint before adding retrieval, identity and tool calls so bottlenecks remain interpretable. Require approved prompts, an existing-service baseline and a clear failure policy. Measure response distributions and completed requests under steady and burst demand; score answers independently. Exercise access denial and unavailable downstream tools in a separate control test. The proof of value should record where acceptable responsiveness ends and whether the application still meets its task rubric, not merely report maximum device output.

Assign the application owner the quality decision and platform operations the capacity decision. Build a maintained test corpus, load profile, reporting dashboard and regression procedure. Dependencies include representative users, approved test records and staff time for independent scoring. Train support teams to distinguish slow generation, incorrect answers and downstream tool failures. Review the proposed release against all three conditions before inviting broader adoption. Proposed acceptance criteria are completion of the agreed workload within local latency thresholds, satisfactory rubric scores and recoverable behavior during overload. Risks include unrealistic prompts, hidden reviewer labor and losing comparability when configurations change.