Selecting accelerators for AI workloads: GPU vs TPU vs NPU vs FPGA vs CPU,
and the metrics that actually decide it — memory capacity & bandwidth, TOPS/
FLOPS, interconnect, and cost/Watt. Architect-level hardware-fit reasoning.
USE WHEN: choosing AI hardware/accelerators, "which GPU", "TPU vs GPU", "NPU",
"FPGA", "HBM/memory bandwidth", "TOPS", "cost per token", VRAM sizing for a
model, training vs inference hardware, accelerator interconnect.
DO NOT USE FOR: serving software topology (use `inference-serving-topology`);
on-device runtimes (use `edge-inference`); generic CPU perf (use
systems/hardware-aware-design).
