Editing AI Chips and Hardware Accelerators (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Expert hardware evaluation goes beyond peak FLOPS numbers:

'''Roofline model analysis''': For a given model and hardware, determine whether the workload is compute-bound (FLOPS-limited) or memory-bandwidth-bound. The roofline model plots operational intensity (FLOPS/byte) against performance, revealing the actual bottleneck.

'''MFU (Model FLOPs Utilization)''': The ratio of measured throughput to theoretical peak. For transformer training on H100, good practitioners achieve 40–60% MFU. Below 20% indicates significant efficiency problems. MFU is the standard metric for training efficiency in the research community (popularized by PaLM).

'''Total Cost of Ownership (TCO)''': Cloud GPU pricing varies dramatically by provider and contract. For large-scale training, comparing $/PFLOP-hour and factoring in spot/preemptible pricing is essential. On-premise vs. cloud break-even analysis depends heavily on utilization.

Expert practitioners profile at the '''kernel level''' using Nsight Compute (NVIDIA) to understand exactly which operations are bottlenecked, enabling targeted optimization.
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