P100 vs Quadro P5000

Memory bandwidth marks the primary spec divergence: the P100 achieves 732 GB/s with HBM2, over 2.5 times the Quadro P5000's 288 GB/s GDDR5X. This gap impacts memory-bound workloads like model training, where the P100 sustains larger batch sizes without bottlenecks, improving effective throughput by reducing data transfer latencies.

Compute performance shows minimal difference, with the P100 at 9.3 TFLOPS FP16 and FP32 versus 8.9 TFLOPS on the Quadro P5000. For deep learning training, equal FP16 and FP32 rates on both enable mixed-precision workflows without penalty, though the P100's bandwidth edge accelerates gradient computations and optimizer steps in large models. Inference benefits similarly, as higher bandwidth supports bigger batches for lower latency in serving pipelines.

Power draw differs at 250W TDP for the P100 against 180W for the Quadro P5000, implying denser cloud packing for the latter but higher absolute performance density on the former. NVLink on the P100 facilitates efficient multi-GPU communication, absent on the Quadro P5000, enhancing scaled training efficiency.

The P100 suits high-throughput compute tasks such as machine learning training on large datasets. Its 732 GB/s bandwidth handles memory-intensive operations better than the Quadro P5000's 288 GB/s, supporting larger batch sizes and faster iterations. NVLink enables seamless multi-GPU setups, ideal for distributed training.

Cost drives selection in cloud environments, with pricing from $0.07 per hour average $0.25 per hour across 3 offers, versus $0.78 per hour for the Quadro P5000.

The Quadro P5000 fits professional visualization and CAD workflows requiring certified drivers. Its 180W TDP allows deployment in power-constrained workstations or denser cloud instances compared to the P100's 250W.

Availability matters, as 6 live cloud offers at $0.78 per hour provide more options than the P100's 3 offers.