GH200 vs H200

Compute performance remains equivalent between the GH200 and H200: both achieve 1979 TFLOPS in FP16 for accelerated training and 3958 TFLOPS in FP8 for inference tasks. The FP16 to FP32 ratio of approximately 30:1 on both GPUs favors mixed-precision training, where FP16 handles forward and backward passes efficiently while FP32 ensures numerical stability in optimizations. This parity means raw throughput differences stem from memory rather than cores.

The H200's 141 GB HBM3e VRAM surpasses the GH200's 96 GB, enabling larger models or batch sizes without multi-GPU sharding. For instance, a 70 billion parameter model at FP16 requires about 140 GB, fitting natively on the H200 but demanding distribution on the GH200. Memory bandwidth edges higher at 4800 GB/s on the H200 versus 4000 GB/s on the GH200, reducing latency in data-heavy operations like transformer attention layers and supporting 20 percent larger effective batch sizes before bandwidth saturation.

Power efficiency tilts toward the H200 with its 700W TDP compared to 900W, yielding better performance per watt in sustained workloads. The GH200's NVLink-C2C excels in CPU-GPU coherent tasks, minimizing data copies that could bottleneck hybrid applications.

The GH200 excels in workloads requiring tight CPU-GPU integration via NVLink-C2C, such as scientific simulations blending CPU orchestration with GPU acceleration. Its 96 GB HBM3 VRAM suffices for models up to 40 billion parameters at FP16, and the 900W TDP sustains peak 1979 TFLOPS FP16 without thermal throttling in high-density racks. Limited cloud offers at $1.99 per hour make it ideal for specialized, low-competition deployments.

Opt for the H200 in memory-intensive AI tasks, where 141 GB HBM3e VRAM accommodates models exceeding 70 billion parameters without partitioning. The 4800 GB/s bandwidth supports larger batch sizes, accelerating training convergence by up to 20 percent over the GH200's 4000 GB/s. With 9 cloud offers starting at $0.49 per hour and InfiniBand support, it scales efficiently in multi-node clusters for production inference.