Published benchmarks can narrow your options, but the decision that matters is how hardware performs on your models, your data and your operating constraints. A well-run evaluation is not complicated, yet many teams skip steps and end up with results they cannot defend. This article lays out a plan you can adapt, using principles that stay valid as hardware changes.

Step One: Define the Decision

Begin with the decision the evaluation must support. Examples: “Which of two accelerators should we buy for serving?” or “Is it worth migrating our training jobs to a different platform?” A clear decision shapes everything else, including which metrics matter and what counts as a pass.

Write down the decision, the stakeholders, the deadline and the budget for the evaluation itself. A time-boxed pilot beats an open-ended one.

Step Two: Choose Representative Workloads

Select a small set of workloads that cover your real usage:

• Your most important model in production or planned for it.
• A second model of a different type, if you run several.
• One awkward case, such as a custom operator, long inputs or variable input sizes.

Use realistic data. Synthetic inputs can hide problems, especially with variable lengths or unusual distributions. If privacy rules limit data use, create an anonymized sample that preserves the characteristics that affect performance. Where privacy or legal requirements apply, consult qualified professionals.

Step Three: Set Success Criteria Up Front

Decide before testing what you will measure and what is acceptable. Typical criteria:

• Quality: accuracy or output quality within a tolerance of your baseline.
• Latency: a target for typical and worst-case responses under expected load.
• Throughput: the work per unit of time needed to meet demand.
• Efficiency: energy or cost per unit of work.
• Effort: engineering time to port, tune and operate.
• Stability: error rates and behavior over long runs.

Setting these first prevents the common failure of choosing criteria after seeing results, which tends to favor whichever option you already liked.

Step Four: Control the Variables

Fair comparison needs consistent conditions. Keep the model, data, precision target, software versions and measurement method the same across systems wherever possible. When a difference is unavoidable, such as a vendor-specific compiler, record it.

Run each test multiple times and warm up the system first, since early runs often include one-time compilation or caching costs. Report spread, not just a single best run.

Step Five: Measure More Than Speed

Add quality and operations to your record. Check that outputs match your baseline within tolerance, especially if you change precision. Log how long setup took, which errors occurred, how clear the documentation was and how responsive the vendor’s support was. These facts are part of the real cost.

Also test under a realistic load pattern, including bursts and idle periods, not only steady maximum load. Many services spend much of their time in mixed conditions.

Evaluation Plan Template

Phase	Activity	Output
Define	State the decision, constraints and deadline	One-page scope
Select	Choose workloads and data	Test set description
Criteria	Set thresholds for quality, latency, throughput, efficiency, effort	Pass/fail rubric
Prepare	Fix software versions and configurations	Reproducible environment
Run	Execute repeated tests, including load patterns	Raw logs
Validate	Check output quality against baseline	Quality report
Analyze	Normalize and compare using the rubric	Comparison summary
Decide	Review with stakeholders, document assumptions	Decision record

A Hypothetical Example

A team wants to choose between two accelerators for serving an internal summarization service. They define the decision, set a latency target for typical documents, require summaries to score within a tolerance of their current system on a reviewed sample, and cap engineering effort at a few weeks.

They test a short-document set, a long-document set, and a mixed traffic replay. Both candidates meet quality thresholds. One meets the latency target on short documents but struggles on long ones because of memory limits, while the other meets both but required extra porting work for a custom component. The decision record states these trade-offs plainly: the second option costs more upfront effort but satisfies both document types. Because everything was written down, the team can explain the choice to leadership and revisit it if documents get longer.

Pitfalls to Avoid

• Testing only the happy path and skipping long, rare or malformed inputs.
• Comparing different software versions without noting it.
• Letting vendors tune their side extensively while your side runs defaults.
• Skipping repeated runs and trusting one number.
• Forgetting to measure the cost of the people’s time involved.
• Declaring victory without checking output quality.

Frequently Asked Questions

How long should an evaluation take? Long enough to cover representative workloads and repeated runs, but time-boxed. For many teams, a few weeks is realistic. Scope depends on how many models and configurations you must test.

Can I run the evaluation in the cloud first? Often yes, and it is usually cheaper than buying hardware. Confirm the cloud instance matches what you would buy or deploy, and check the provider’s current documentation for the specifics.

How do I handle vendor-run proofs of concept? They can save time, but insist on your own workloads, the same success criteria and access to raw results. If possible, repeat key tests yourself.

What if the results are close? Then other factors decide: software maturity, supply, support, operational fit and the cost of migration. Close performance is itself useful information.

How do I keep the evaluation useful later? Script it. A repeatable harness lets you retest when software updates, models change or new hardware appears, at a small fraction of the original effort.

Next Step

Draft the one-page scope and the pass/fail rubric before you request any hardware access. Share it with stakeholders for agreement first. Once the rubric is agreed, the testing itself becomes straightforward and the decision far easier to defend.