GH200 Grace Hopper vs H200 NVL

Compute throughput remains identical across both GPUs: 1979 TFLOPS FP16 and 3958 TFLOPS FP8 suit large-scale training and inference, while 67 TFLOPS FP32 handles general-purpose tasks. This parity means raw FLOPS do not differentiate them; real-world gains stem from memory specs. The H200's 141 GB HBM3e VRAM versus GH200's 96 GB HBM3 enables larger batch sizes in LLM training, reducing overhead from model swapping. For inference, this supports bigger models without quantization, maintaining accuracy at scale. Higher 4800 GB/s bandwidth on H200 accelerates data movement: it boosts effective throughput by 20 percent in memory-bound scenarios like fine-tuning, where 4000 GB/s on GH200 limits batch scaling. GH200's 900W TDP demands more cooling than H200's 700W, impacting density in clusters. NVLink-C2C on GH200 enhances CPU-GPU cohesion for hybrid workloads.

The GH200 Grace Hopper suits workloads requiring tight CPU-GPU integration via NVLink-C2C. Scientific simulations blending CPU preprocessing with 1979 TFLOPS FP16 GPU acceleration benefit from the Grace CPU, avoiding data transfer latencies seen in discrete setups. At 900W TDP, it fits dense SXM racks for HPC clusters prioritizing unified memory over capacity.

The H200 NVL excels in memory-intensive AI tasks with 141 GB HBM3e and 4800 GB/s bandwidth. LLM inference at scale leverages the extra 45 GB VRAM for larger models, while lower $0.50 per hour pricing and 700W TDP reduce costs in cloud deployments. InfiniBand support aids multi-node scaling for training.