P100 vs RTX 5090

Compute performance defines the core disparity between these GPUs. The P100 achieves 9.3 TFLOPS in both FP16 and FP32, adequate for single-precision tasks in its era but limited for modern deep learning. The RTX 5090 surges to 419 TFLOPS FP16 and 105 TFLOPS FP32, with 838 TFLOPS FP8 support: this FP16/FP32 delta excels in training, where mixed-precision techniques halve memory usage and double speed without accuracy degradation; inference benefits similarly from FP8 for quantized models.

Memory specifications profoundly impact real-world usage. RTX 5090's 1792 GB/s bandwidth, over twice the P100's 732 GB/s, sustains larger batch sizes in training loops, minimizing data loading bottlenecks and boosting iterations per epoch. The 32 GB GDDR7 VRAM versus 16 GB HBM2 enables handling models exceeding 16 GB directly, avoiding multi-GPU complexity. Power draw reflects this: 575W TDP for RTX 5090 demands robust cooling, while P100's 250W suits lighter deployments.

Interconnects highlight deployment contexts: P100's NVLink facilitates multi-GPU scaling in SXM2 or PCIe forms, whereas RTX 5090 relies on PCIe 5.0 in PCIe form, prioritizing single-GPU potency over legacy clustering.

The P100 suits budget-constrained legacy applications. At $0.07 per hour starting price and 250W TDP, it handles basic FP32 workloads at 9.3 TFLOPS without exceeding low-power envelopes. Ideal for prototyping on older frameworks incompatible with Blackwell or when 16 GB HBM2 suffices for small models across three cloud offers averaging $0.25 per hour.

The RTX 5090 dominates modern AI pipelines. Its 419 TFLOPS FP16 and 32 GB VRAM enable large-scale LLM training and inference at scales P100 cannot match, despite higher 575W TDP and $0.71 per hour average across 19 offers. Choose it for bandwidth-intensive tasks leveraging 1792 GB/s and FP8 at 838 TFLOPS.