A comparison of two AI chips looks easy until you try to make it fair. Marketing material, third-party charts and your own tests are often measuring different things under different conditions. The mistakes below are the ones that most often turn a careful evaluation into a confident wrong answer.

1. Comparing Peak Throughput Instead of Delivered Throughput

Peak figures describe what the arithmetic units could do if they were never starved of data. Real models spend a great deal of time waiting on memory, synchronising, or running operations the chip handles poorly. A common trap is ranking chips by peak numbers and then discovering that the ranking inverts on your model.

Ask for results on workloads similar to yours, or better, measure your own. If you cannot, treat peak figures as an upper bound, not an estimate.

2. Comparing Different Numeric Precisions

One chip’s headline number may be quoted at a low-precision format while another’s is quoted at a higher one. Those are not the same job. Lower precision can run much faster, but only if your model tolerates it without unacceptable accuracy loss.

Before comparing, fix the precision your model needs in production, verify accuracy at that precision, and compare chips at that setting. A speed gain that requires a quality loss you cannot accept is not a gain.

3. Ignoring Memory Capacity and Bandwidth

Compute is the number everyone quotes, and memory is the constraint that often decides. If the model weights and working state do not fit, you need more devices or heavy workarounds, and the comparison changes completely. Even when everything fits, bandwidth frequently sets the ceiling for generation-style workloads.

Put memory size and bandwidth in the same row as compute on every comparison sheet. A chip that wins on one and loses on the other needs a closer look, not a quick ranking.

4. Treating the Software Stack as an Afterthought

The compiler, the runtime, the profiling tools and the library coverage determine whether the hardware’s capability is reachable. Teams sometimes pick a chip for its numbers and then spend months chasing missing operators or poor kernel quality.

Check how your specific operators, framework versions and deployment tooling are supported. Vendor documentation changes frequently, so confirm against the current release rather than a blog post or an old forum thread.

5. Benchmarking With Unrealistic Inputs

Tests with a single fixed batch size, short inputs, or synthetic data can flatter a chip. Production traffic usually has mixed sizes, bursts and idle periods. A chip tuned for large steady batches may disappoint on small, latency-bound requests, and the reverse can also be true.

Build a representative request mix, including awkward cases, and replay it on each candidate.

6. Forgetting Power, Cooling and Space

A chip that is faster but needs a different power delivery or cooling setup can add facility cost that dwarfs the hardware price difference. This matters most in dense deployments and on-premises racks, but even small systems can hit thermal limits that cause throttling.

Include power draw under your real workload, not only the rated maximum, and ask what cooling the vendor requires. Check current specifications from the vendor, since they vary by configuration.

7. Comparing Hardware Price Instead of Cost per Useful Result

The purchase or rental price alone says little. What matters is the cost to complete the work you care about: a training run, a million requests, a day of production inference. A cheaper device that takes much longer, or needs more units to fit the model, may cost more per result.

Choose a unit of work, measure the time and energy it takes on each candidate, and compute cost around that. Include engineering time, because a difficult toolchain has a real price.

A Quick Audit Checklist

Question	Why it matters	Red flag
Is precision identical across candidates?	Prevents apples-to-oranges speed claims	Different formats in the same chart
Does the model fit in device memory?	Determines device count and design	“Fits” claimed without workload details
Are results from your model class?	Architectures stress chips differently	Only generic or unnamed benchmarks
Are batch size and input length stated?	Changes results significantly	Only the best-case setting reported
Is accuracy checked at that precision?	Speed means little if quality drops	No accuracy figure alongside speed
Is the software stack current and supported?	Determines reachable performance	Key operators missing or experimental

A Hypothetical Example

Suppose a team is comparing two accelerators for a text-generation service. The first has the higher quoted compute, but the figure is at a lower precision than the team plans to use. The second has less compute but more memory bandwidth.

On a spec sheet the first looks better by a wide margin. When the team reruns both at the precision they actually need, using a request mix recorded from their staging environment, the second keeps pace on long outputs because generation is bandwidth-bound, and it needs fewer devices because the model fits more comfortably. The ranking reversed because the test finally matched the workload.

Nothing exotic happened. The team simply stopped trusting numbers that were measured for someone else’s job.

Frequently Asked Questions

Are vendor benchmarks useless? No, but they answer the vendor’s question, not yours. They are useful for sanity checks and for knowing what is achievable under ideal conditions. Confirm anything important with your own model.

How many chips should I actually test? Two or three serious candidates is usually enough. More than that tends to dilute effort, because each one needs proper setup and tuning to be a fair test.

Should I tune each candidate for its best result? Give each a reasonable, comparable level of effort, and document what you did. Heavy tuning on one and default settings on another produces a misleading result.

What if I cannot get access to the hardware? Cloud rentals or vendor evaluation programs are the usual route. If neither is possible, be explicit that your comparison is based on published figures and treat the conclusion as provisional.

Does energy efficiency matter if power is not billed to me? Often yes, because power and cooling limits can cap how many devices you can deploy, even when you never see the electricity bill.

Next Step

Take your current shortlist and add a column for each of the seven items above. Wherever a cell is empty or based on a vendor chart, schedule a measurement on your own workload before the comparison is used to justify a purchase.