A16 vs GH200

The FP16 dense figure of 989 TFLOPS on the GH200 exceeds the 17.9 TFLOPS dense FP16 figure on the A16 by a wide margin and supports larger batch sizes during training or inference. The FP32 figure of 67 TFLOPS on the GH200 likewise exceeds the 4.5 TFLOPS FP32 figure on the A16 and favors scientific workloads that rely on single precision. Memory bandwidth of 4000 GB/s on the GH200 compared with 200 GB/s on the A16 permits substantially larger batches before memory capacity or throughput becomes limiting. When sparsity is enabled the GH200 reaches 1979 TFLOPS FP16 with sparsity while the A16 reaches 35.9 TFLOPS FP16 with sparsity so the same ordering holds for both dense and sparse modes.

The A16 suits deployments that require 250W TDP and PCIe connectivity with 16 GB GDDR6 VRAM. Workloads that fit within 16 GB and need only 17.9 TFLOPS FP16 dense or 35.9 TFLOPS FP16 with sparsity benefit from the lower power draw and simpler integration. Inference tasks on modest models or edge nodes that cannot accommodate 900W TDP or SXM form factor select the A16.

The GH200 suits workloads that require 96 GB HBM3 VRAM and 4000 GB/s bandwidth together with 989 TFLOPS FP16 dense or 1979 TFLOPS FP16 with sparsity. Training or inference of large models that exceed 16 GB capacity and need the 67 TFLOPS FP32 performance select the GH200. Systems already equipped for SXM modules and NVLink-C2C interconnect gain the full benefit of the 900W TDP rating.