Quadro P5000 vs RTX 4080

The FP32 performance shows the RTX 4080 at 48.7 TFLOPS against the Quadro P5000 at 8.9 TFLOPS which translates to over five times the throughput for general compute workloads on the newer card. For tensor operations the RTX 4080 delivers dense FP16 at 97.5 TFLOPS while the Quadro P5000 has no published FP16 figure limiting its suitability for mixed precision training. Memory bandwidth at 717 GB/s on the RTX 4080 versus 288 GB/s on the Quadro P5000 allows larger batch sizes during inference without swapping to system memory. When comparing dense FP16 figures the RTX 4080 reaches 97.5 TFLOPS dense against the older card with no equivalent metric available. Sparsity on the RTX 4080 further scales dense FP16 to 195 TFLOPS with sparsity yet remains unavailable on the Quadro P5000.

The Quadro P5000 suits scenarios with strict power limits at 180W TDP where the RTX 4080 at 320W exceeds available cooling or electricity budgets. Older PCIe 3.0 systems benefit from direct compatibility without upgrades while the 16 GB GDDR5X configuration handles legacy professional workloads at 8.9 TFLOPS FP32.

The RTX 4080 excels in modern tensor workloads with dense FP16 at 97.5 TFLOPS and FP32 at 48.7 TFLOPS enabling faster training cycles than the 8.9 TFLOPS on the Quadro P5000. Higher memory bandwidth of 717 GB/s supports larger batches in inference compared to 288 GB/s. PCIe 4.0 interconnect reduces data transfer bottlenecks in multi GPU setups for scientific computing tasks.