GTX 1080 vs Quadro P5000

Compute performance aligns closely between the GTX 1080 and Quadro P5000 at 8.9 TFLOPS for both FP16 and FP32, indicating equivalent throughput for half-precision training and single-precision inference tasks. This parity suggests similar iteration speeds in neural network training or inference runs, assuming models fit within VRAM limits.

VRAM differs significantly: 8 to 11 GB on the GTX 1080 restricts batch sizes for large models, potentially requiring gradient accumulation, whereas 16 GB on the Quadro P5000 supports bigger batches and datasets without splitting. Memory bandwidth favors the GTX 1080 at 320 GB/s over 288 GB/s, accelerating data loading and reducing bottlenecks in bandwidth-sensitive workloads like high-resolution image processing.

In practice, FP16/FP32 equivalence benefits mixed-precision training equally, but VRAM governs scalability: smaller batches on GTX 1080 suit lightweight inference, while Quadro P5000 excels in memory-bound training scenarios.

The GTX 1080 suits cost-sensitive deployments where models fit within 8 to 11 GB VRAM. At $0.30 per hour starting price and 320 GB/s bandwidth, it outperforms the Quadro P5000 in data transfer for inference or fine-tuning small networks, offering better value at $0.45 per hour average.

Opt for the Quadro P5000 when workloads demand 16 GB VRAM for larger models or datasets. Its capacity supports extensive batch sizes in training, available across 6 cloud offers at $0.78 per hour, prioritizing memory over the GTX 1080's bandwidth edge.