A100 vs Quadro P5000

FP32 throughput on the A100 reaches 19.5 TFLOPS while the Quadro P5000 reaches 8.9 TFLOPS producing a ratio of roughly 2.2 times higher dense FP32 capability on the A100. Memory bandwidth on the A100 at 2039 GB/s exceeds the Quadro P5000 at 288 GB/s by a factor of approximately 7.1 allowing larger batch sizes before memory becomes the limiter. The A100 further supplies dense FP16 at 312 TFLOPS and FP16 with sparsity at 624 TFLOPS along with dense INT8 at 624 TOPS and INT8 with sparsity at 1248 TOPS none of which appear for the Quadro P5000. Higher bandwidth and these dense FP16 figures enable the A100 to sustain larger training batches and faster inference iterations than the Quadro P5000 can achieve under identical precision constraints. TDP of 400W on the A100 versus 180W on the Quadro P5000 indicates greater power draw accompanies the performance gains.

The A100 suits LLM training and fine tuning workloads that require dense FP16 at 312 TFLOPS or INT8 with sparsity at 1248 TOPS. Its 40 to 80 GB HBM2e memory and 2039 GB/s bandwidth support larger models and batch sizes than the 16 GB limit on the Quadro P5000. NVLink and InfiniBand interconnects further favor multi GPU scaling on the A100.

The Quadro P5000 fits lighter scientific computing tasks where FP32 at 8.9 TFLOPS meets requirements and 180W TDP keeps power draw low. Its PCIe form factor and 16 GB GDDR5X memory suffice for smaller inference jobs that do not need the 2039 GB/s bandwidth or dense FP16 figures of the A100.