VISION case study reports research throughput gains; cost and utilization methods remain incomplete

Engage research-computing leaders, principal investigators, finance and facilities around experiments constrained by current capacity. Ask which work cannot run today, how long researchers wait, and whether faster computation would shift the bottleneck to laboratory validation. A bounded workload-assessment engagement could compare owned and rented capacity using the same planned pipeline. The value hypothesis is enabling a larger or more useful experiment within a funding window. Treat the reported screening run as a discovery example. Do not promise the same throughput, improved clinical outcomes, net savings or broad access without a local workload and total-cost evaluation.

Capture the actual screening recipe, input data, model version, precision, GPU allocation and output-validation method. Compare a representative subset on the existing and proposed environments with consistent scientific criteria. Measure end-to-end elapsed time, queue delay, storage behavior and failed jobs, not GPU activity alone. Confirm data movement, approved containers and access boundaries before benchmarking. If a coding assistant prepares job scripts, inspect its changes and restrict its privileges. A useful proof of value preserves reproducible artifacts and independently reviewed scientific outputs. Separate computational throughput from the subsequent cost and quality of laboratory validation.

The research-computing service owner should coordinate platform staff, scientific users and finance. Implement workload onboarding, data staging, job templates, allocation rules and outcome reporting. Dependencies include storage throughput, trained researchers, laboratory capacity and a sustainable operating budget. Provide accessible submission instructions and a supported onboarding path for less experienced teams. Review data authorization and scientific validity before expanding use. Proposed acceptance criteria are successful reproduction of an agreed screening subset, complete configuration records and documented turnaround against the existing process. Risks include an unrepresentative first user, queue growth and downstream validation costs erasing apparent compute benefits.