P100 vs RTX 5080

The RTX 5080 dense FP16 performance of 112.6 TFLOPS exceeds the P100 dense FP16 performance of 18.7 TFLOPS by a factor of six. This ratio indicates that training or inference steps using dense FP16 arithmetic complete substantially faster on the RTX 5080. The same pattern holds for dense FP32 where the RTX 5080 at 56.3 TFLOPS surpasses the P100 at 9.3 TFLOPS. Memory bandwidth of 960 GB/s on the RTX 5080 versus 732 GB/s on the P100 permits larger batch sizes before memory capacity or transfer limits are reached. When sparsity is available the RTX 5080 further widens the gap with 225.1 TFLOPS sparse FP16 compared with the P100 which publishes no sparse figure.

The P100 suits scientific computing workloads that fit within 16 GB and benefit from its 732 GB/s bandwidth together with NVLink interconnect. Its 250 W TDP also reduces power draw relative to the 360 W TDP of the RTX 5080 when continuous operation is required. Organizations already operating P100 nodes in existing clusters therefore continue to run compatible codes without hardware replacement.

The RTX 5080 suits LLM inference and fine tuning jobs that exploit its 112.6 TFLOPS dense FP16 and 225.1 TFLOPS dense FP8 ratings. Its 960 GB/s bandwidth supports larger batches than the P100 bandwidth allows. New deployments therefore select the RTX 5080 when maximum throughput per card is the primary requirement.