GH200 vs P100

The GH200 achieves 989 TFLOPS FP16 dense performance while the P100 achieves 18.7 TFLOPS FP16 dense performance. This gap means the GH200 completes FP16 dense matrix operations more than fifty times faster than the P100 for both training and inference tasks. The GH200 also supplies 1979 TFLOPS FP16 with sparsity whereas the P100 supplies no sparsity figure for FP16. Higher memory bandwidth of 4000 GB/s on the GH200 versus 732 GB/s on the P100 supports larger batch sizes during training because data moves more rapidly between memory and compute units. FP32 throughput on the GH200 reaches 67 TFLOPS compared with 9.3 TFLOPS on the P100 so workloads that rely on single precision arithmetic also finish faster on the newer GPU.

The GH200 suits LLM training workloads that require 96 GB HBM3 memory and 989 TFLOPS FP16 dense throughput. Its 4000 GB/s bandwidth further enables larger batch sizes than the P100 bandwidth of 732 GB/s allows. Organizations running scientific computing jobs that need 67 TFLOPS FP32 performance select the GH200 over the older GPU.

The P100 fits inference tasks on small models that fit within 16 GB HBM2 memory and require only 18.7 TFLOPS FP16 dense performance. Its 250W TDP makes the P100 viable in power constrained environments where the GH200 900W TDP exceeds available capacity. Legacy codebases already validated on Pascal architecture continue to run on the P100 without modification.