GB300 SXM6 vs GH200 Grace Hopper

The FP16 performance gap reveals clear advantages for training and inference tasks: GB300 SXM6 achieves 2250 TFLOPS compared to GH200's 1979 TFLOPS, a 14 percent increase that accelerates large language model training cycles. FP32 throughput at 90 TFLOPS on GB300 SXM6 outpaces GH200's 67 TFLOPS by 34 percent, benefiting scientific simulations requiring precise single-precision calculations. FP8 capabilities emphasize inference efficiency, where GB300 SXM6 delivers 4500 TFLOPS against 3958 TFLOPS, supporting higher throughput for deployed models. Memory bandwidth profoundly impacts real-world usage: 12000 GB/s on GB300 SXM6 permits batch sizes three times larger than GH200's 4000 GB/s limit, reducing overhead in data loading for massive datasets. The 288 GB HBM3e VRAM on GB300 SXM6 accommodates models exceeding 100 billion parameters intact, while GH200's 96 GB HBM3 often necessitates model parallelism. Higher 1400W TDP on GB300 SXM6 demands advanced cooling, contrasting GH200's more manageable 900W for dense clusters.

GB300 SXM6 excels in extreme-scale AI training where datasets and models demand vast resources: its 288 GB HBM3e VRAM and 12000 GB/s bandwidth handle unpartitioned trillion-parameter models. Scenarios include research labs pioneering next-generation LLMs, leveraging 2250 TFLOPS FP16 for faster convergence. NVSwitch interconnect scales multi-GPU systems seamlessly for petabyte-scale computations.

GH200 suits production environments prioritizing availability and efficiency: live cloud offers start at $1.99 per hour with average $3.59 per hour across four providers. Lower 900W TDP enables higher rack density than GB300 SXM6's 1400W, ideal for inference services running 96 GB models at 3958 TFLOPS FP8. PCIe 5.0 support integrates easily into existing data centers.