AI Chip Comparator

Accelerator marketing leads with one number — peak FLOPS — but choosing a chip on FLOPS alone is how teams end up with the wrong hardware. Real accelerator selection spans five axes that pull against each other: raw compute, memory capacity, memory bandwidth, power efficiency, and cost. No chip leads on all of them, so the right answer isn't a single best chip; it's the best chip for the dimensions your workload actually cares about.

Those dimensions weight differently by use. Frontier training is compute-bound and bandwidth-hungry, so FLOPS and TB/s dominate. Large-model serving is memory-capacity- and bandwidth-constrained — the chip that holds the model and streams it fast beats the one with more raw FLOPS. A power-limited datacenter cares most about performance-per-watt, because power is both the physical ceiling and the recurring bill. And a throughput-bound budget cares about cost-per-FLOP, where a cheaper, slower chip can deliver more compute per dollar than a flagship.

Two derived metrics cut through the spec sheet. Performance-per-watt — FLOPS divided by power — is the datacenter-scale efficiency number, and at scale it often matters more than peak speed. Cost-per-FLOP — price divided by throughput — normalizes wildly different chips onto a single value axis, and it's frequently the cheaper chip, not the flagship, that wins it. This comparator surfaces both alongside the raw specs.

One caveat the hardware numbers can't capture: software ecosystem. CUDA's maturity can outweigh a competitor's spec advantage in practice, and porting effort is a real cost. Treat the ranking here as the hardware view, weigh ecosystem and your team's familiarity on top, then size the deployment in the LLM Serving console and cost it in the Accelerator ROI console.