GB300 vs GH200

The GB300's FP16 performance of 2250 TFLOPS exceeds the GH200's 1979 TFLOPS by 14 percent, accelerating mixed-precision training for large language models where FP16 dominates compute. FP32 at 90 TFLOPS on GB300, up 34 percent from 67 TFLOPS on GH200, benefits simulation tasks requiring higher precision. FP8 at 4500 TFLOPS on GB300 supports efficient inference at scale, outpacing GH200's 3958 TFLOPS.

Memory bandwidth of 12000 GB/s on the GB300 permits larger batch sizes in training, reducing iterations and time to convergence compared to GH200's 4000 GB/s limitation. The 288 GB VRAM capacity on GB300 accommodates models exceeding 100 billion parameters intact, while GH200's 96 GB often necessitates model parallelism, increasing complexity. Higher TDP of 1400W on GB300 reflects its density, demanding robust cooling versus GH200's 900W.

NVSwitch and NVLink on GB300 enhance multi-GPU scaling over GH200's NVLink-C2C and PCIe 5.0, minimizing latency in clusters.

Select the GB300 for pioneering large-scale AI training or inference on models demanding over 200 GB VRAM, such as next-generation LLMs, where its 288 GB HBM3e and 12000 GB/s bandwidth enable unprecedented batch sizes. Its 2250 TFLOPS FP16 performance suits hyperscale deployments once available, leveraging Blackwell Ultra efficiencies unavailable in Hopper.

Opt for the GH200 when immediate deployment is essential, with live offers from $1.99 per hour averaging $3.59 per hour across four providers. Its 96 GB HBM3 and 1979 TFLOPS FP16 suffice for current fine-tuning or inference on models under 70 billion parameters, balancing performance with 900W TDP efficiency and NVLink-C2C interconnect.