B200 SXM vs GH200 Grace Hopper

Floating-point performance gaps translate directly to workload speeds. The B200's 4500 TFLOPS FP16 capability more than doubles the GH200's 1979 TFLOPS, speeding up model training where half-precision tensor operations prevail and reducing epochs from days to hours on equivalent datasets. FP32 throughput shows a narrower margin at 90 TFLOPS for B200 versus 67 TFLOPS, adequate for precision-sensitive simulations but secondary in deep learning pipelines.

FP8 performance underscores inference advantages: the B200's 9000 TFLOPS enables quantized model serving at twice the GH200's 3958 TFLOPS pace, critical for real-time applications with high query volumes. Memory differences amplify these effects: 192 GB HBM3e on the B200 supports batch sizes up to double those on the GH200's 96 GB HBM3, minimizing out-of-memory errors in transformer inference. The 8000 GB/s bandwidth versus 4000 GB/s further accelerates data movement, cutting latency in bandwidth-bound scenarios like diffusion models.

Power draw reflects efficiency trade-offs, with the B200 at 1000W TDP exceeding the GH200's 900W, yet delivering superior flops per watt in FP16 and FP8 domains.

Select the NVIDIA B200 for cutting-edge AI training and inference requiring peak capacity. Its 192 GB HBM3e VRAM accommodates full-parameter loading of models exceeding 100 billion parameters, while 4500 TFLOPS FP16 accelerates convergence. High-volume inference leverages 9000 TFLOPS FP8 for low-latency quantized deployments.

Cloud users benefit from 13 live offers starting at $1.71 per hour, offering flexibility for sustained workloads.

Choose the NVIDIA GH200 Grace Hopper for budget-conscious or CPU-integrated tasks. Average pricing of $3.59 per hour across 4 offers undercuts the B200's $4.60 average, suiting proof-of-concept runs. The 900W TDP simplifies cooling versus 1000W, and NVLink-C2C interconnect optimizes Grace CPU-GPU data transfers in hybrid computing.