Choose local AI hardware around a repeatable workload
Before choosing a workstation, define the task you need it to complete. The episode compares local AI development routes; this companion provides a way to evaluate them without confusing model capacity with useful performance.
Write down the workload
Choose a document assistant, coding task or classification queue. Fix the input set, expected outputs and missing-answer cases. Use synthetic material when testing. A machine that produces fluent text still needs to answer the actual task correctly.
Separate capacity from speed
Model weights are only part of memory use. Context, runtime buffers, the operating system and other applications also need room. A model fitting in memory does not establish its response time. Record model revision, quantization, context and concurrency alongside results.
Check the software route
Match the operating system, processor architecture, GPU backend, drivers and dependencies to your intended software. Verify each tool's current supported configurations. A shared hardware family does not guarantee identical application compatibility.
Compare equivalent tests
Run the same questions and settings. Measure answer quality, time to first token, total task time and failures. Include power, noise and portability where they affect your working day. Separate vendor specifications from your observations.
Make the purchase decision
Start with an existing machine if it can run the trial. Ask what measurable problem additional memory or compute would solve. Check the exact configuration, current price, availability and support terms at the seller before buying. This article makes no current price or shipping claim.
The accompanying video reflects research checked October 8, 2026. No cross-machine hardware benchmark was performed. The downloadable evaluation worksheet is a blank planning aid.
