P100 vs RTX 5090

The FP16 to FP32 ratio remains 2 times on both GPUs because the P100 lists 18.7 TFLOPS dense FP16 against 9.3 TFLOPS FP32 and the RTX 5090 lists 209.5 TFLOPS dense FP16 against 104.8 TFLOPS FP32. This ratio indicates that both cards double throughput when workloads stay in dense FP16 rather than FP32 during training or inference steps. Memory bandwidth of 1792 GB/s on the RTX 5090 versus 732 GB/s on the P100 permits larger batch sizes before memory stalls occur in data heavy operations. When sparsity is applied the RTX 5090 reaches 419 TFLOPS in FP16 with sparsity which exceeds the dense FP16 figure of the P100 by more than 22 times and therefore accelerates sparse matrix workloads in inference pipelines.

The P100 suits environments that prioritize a 250W TDP over higher throughput because power constraints limit total system draw. Its NVLink interconnect supports direct GPU to GPU transfers at scales where PCIe 3.0 alone would constrain multi card clusters. Older code bases written for Pascal architecture also run without recompilation on the P100 while newer Blackwell features remain unused.

The RTX 5090 suits workloads that require 209.5 TFLOPS dense FP16 or 104.8 TFLOPS FP32 because these figures exceed the corresponding P100 numbers by more than 11 times. Its 32 GB GDDR7 memory and 1792 GB/s bandwidth accommodate larger models or batch sizes than the 16 GB HBM2 and 732 GB/s on the P100. PCIe 5.0 interconnect further reduces host transfer latency when data movement dominates execution time.