GB300 vs RTX 5080

The GB300 FP16 dense performance of 2250 TFLOPS stands 20 times above the RTX 5080 FP16 dense performance of 112.6 TFLOPS while the GB300 FP16 with sparsity performance of 4500 TFLOPS stands 20 times above the RTX 5080 FP16 with sparsity performance of 225.1 TFLOPS. This delta indicates the GB300 supports substantially larger model training and inference workloads than the RTX 5080. Memory bandwidth of 8000 GB/s on the GB300 compared to 960 GB/s on the RTX 5080 permits larger batch sizes during both training and inference operations. FP32 performance of 75 TFLOPS on the GB300 exceeds FP32 performance of 56.3 TFLOPS on the RTX 5080 and further widens the advantage for precision sensitive scientific workloads.

The GB300 suits large scale LLM training and inference because its 262 GB to 288 GB HBM3e memory capacity accommodates models that exceed the 16 GB GDDR7 capacity of the RTX 5080. Its FP8 dense performance of 4500 TFLOPS and FP4 dense performance of 13500 TFLOPS enable faster iteration on massive datasets than the corresponding 225.1 TFLOPS and 900.4 TFLOPS figures on the RTX 5080.

The RTX 5080 suits power constrained environments because its TDP of 360W remains far below the 1400W TDP of the GB300. Its PCIe form factor and 16 GB GDDR7 memory fit desktop or edge deployments where the SXM6 interconnect and 262 GB to 288 GB HBM3e memory of the GB300 prove unnecessary.