A30 vs GH200

The FP16 dense rating of 989 TFLOPS on the GH200 exceeds the FP16 dense rating of 165 TFLOPS on the A30 by a factor of six. This gap means training sessions complete iterations faster on the GH200 when using dense FP16 operations and inference throughput scales similarly when batch sizes remain constant. The FP16 with sparsity rating of 1979 TFLOPS on the GH200 likewise exceeds the FP16 with sparsity rating of 330 TFLOPS on the A30. Memory bandwidth of 4000 GB/s on the GH200 compared with 933 GB/s on the A30 permits larger batch sizes during both training and inference without memory stalls. The FP32 rating of 67 TFLOPS on the GH200 compared with 10.3 TFLOPS on the A30 further widens the advantage for scientific computing kernels that rely on dense FP32 arithmetic. All tensor comparisons above maintain consistent dense or sparsity labeling to avoid mixing performance modes.

The A30 suits deployments where power limits constrain total system draw to 165W and where PCIe form factor cards fit existing server designs. Workloads that fit within 24 GB HBM2 memory and that tolerate FP16 dense performance of 165 TFLOPS benefit from the lower TDP without excess capacity. Inference tasks that process moderate batch sizes and that operate under INT8 dense ratings of 330 TOPS find adequate throughput on the A30 when energy efficiency takes priority over peak speed.

The GH200 suits large model training that requires 96 GB HBM3 memory and FP16 dense performance of 989 TFLOPS to maintain acceptable iteration times. Scientific computing applications that need the FP32 rating of 67 TFLOPS and memory bandwidth of 4000 GB/s complete simulations faster on the GH200. Inference at extreme scale that utilizes the INT8 dense rating of 1979 TOPS and the SXM form factor with NVLink-C2C interconnect favors the GH200 when throughput targets exceed A30 capabilities.