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For local AI models, the first hardware question may not be speed: it is whether the model fits in memory at all. A comparison by The AI Engineer says memory size determines what can run, while memory speed affects how quickly it writes.
Why it mattersThe article contrasts the Mac Studio’s 128GB memory pool for larger models, the DGX Spark’s CUDA-compatible workloads and the RTX 5090’s faster performance on models that fit its 32GB card. That is a useful buying lens, though the supplied comparison gives no test results to settle which system is best overall.
Discuss: When choosing local AI hardware, would you prioritise fitting a larger model, faster output or better value?
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