GH200 vs H100

Peak compute metrics reveal parity between the GH200 and H100: both deliver 1979 TFLOPS in FP16 for mixed-precision training and 3958 TFLOPS in FP8 for inference acceleration, ensuring equivalent raw throughput in AI models. The FP16 and FP32 figures of 1979 TFLOPS and 67 TFLOPS respectively support large-scale neural network training without differentiation in flops-limited scenarios, but real-world training benefits from the GH200's expanded 96 GB VRAM, enabling larger batch sizes than the H100's 80-94 GB limit.

Memory bandwidth emerges as a key differentiator: the GH200's 4000 GB/s sustains higher data throughput for memory-intensive operations, reducing bottlenecks in transformer models compared to the H100's 3350 GB/s. This advantage translates to faster iteration in LLM training with massive datasets, where bandwidth constraints limit batch sizes on the H100. Inference workloads see similar gains, as FP8 performance matches but higher bandwidth minimizes latency spikes.

Power consumption varies significantly: the GH200's 900W TDP demands robust cooling versus the H100's 700W, impacting cloud deployment density and costs in prolonged runs.

The GH200 suits scenarios requiring maximum memory capacity, such as training LLMs exceeding 80 GB model weights, where its 96 GB HBM3 VRAM and 4000 GB/s bandwidth enable larger batches without splitting across GPUs. Integrated NVLink-C2C connectivity excels in CPU-GPU hybrid workloads like scientific simulations demanding seamless data transfer.

Opt for the GH200 in environments prioritizing peak memory performance over availability, despite its $1.99 per hour starting price across limited 2 offers.

The H100 proves ideal for cost-conscious deployments, offering entry pricing from $0.80 per hour across 22 live offers, far outpacing the GH200's availability. Its 700W TDP and flexible form factors including PCIe and NVL support broader integration in diverse cloud setups.

Choose the H100 for general AI inference and fine-tuning where 80-94 GB VRAM suffices, leveraging identical 1979 TFLOPS FP16 performance at lower power and higher option count.