NVIDIA GH200 144GB HBM3e vs NVIDIA H200 SXM 141GB

Both GPUs launched in 2024: the GH200 on Hopper and the H200 on Hopper.

Dense throughput for the GH200 against the H200: FP64 34 vs 34 TFLOPS, FP32 67 vs 67 TFLOPS, FP16 990 vs 990 TFLOPS, FP8 1,979 vs 1,979 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.

Memory is 144 GB against 141 GB, fed at 4.9 TB/s versus 4.8 TB/s. More memory per GPU means larger models fit before you have to shard across cards, which often matters more than raw TFLOPS for inference.

Power budgets are 1.0 kW for the GH200 and 700 W for the H200. At FP32 that works out to 0.07 against 0.10 TFLOPS per watt.

For LLM training and inference, weight the FP8 and FP16 rows and memory capacity most heavily. For scientific and HPC workloads, the FP64 row is the one to read.