GB300 vs RTX PRO 6000

The GB300 delivers 2250 TFLOPS FP16 dense compared with 503.8 TFLOPS FP16 dense on the RTX PRO 6000. This gap means the GB300 processes larger matrix operations in the same time for training workloads that rely on dense FP16 arithmetic. The RTX PRO 6000 delivers 126 TFLOPS FP32 compared with 75 TFLOPS FP32 on the GB300 so the RTX PRO 6000 completes single precision scientific kernels faster. Memory bandwidth of 8000 GB/s on the GB300 versus 1792 GB/s on the RTX PRO 6000 allows the GB300 to sustain larger batch sizes during inference without stalling on data movement when both GPUs run dense FP16 workloads.

The GB300 suits LLM training runs that require 262 to 288 GB VRAM to hold full model states. Its 8000 GB/s bandwidth and 2250 TFLOPS FP16 dense rating support larger batch sizes than the RTX PRO 6000 can accommodate. Organizations running multi node clusters benefit from its NVLink and InfiniBand interconnect options.

Choose the RTX PRO 6000 for single GPU inference and fine tuning of anything that fits in 96 GB of GDDR7, for image and video generation, and for FP32 heavy code, where its 126 TFLOPS of FP32 beats the 75 TFLOPS of the GB300. Its 1007.6 TFLOPS of dense FP8 and 1792 GB/s of bandwidth serve mid sized models comfortably, and as a 600W PCIe 5.0 card it is available as a single unit in a way the GB300 is not. Do not pick it for multi GPU training: it has no NVLink, so cross card traffic goes over PCIe and the 4.5 times bandwidth gap to the GB300 compounds.