P100 vs RTX 2080

The RTX 2080 holds a compute edge: its 10.1 TFLOPS FP16 and FP32 surpass the P100's 9.3 TFLOPS by about 8 percent. This advantage translates to faster training and inference iterations for models fitting within 8 to 11 GB VRAM, reducing epoch times in frameworks like TensorFlow or PyTorch.

Memory specifications diverge significantly: P100's 16 GB HBM2 and 732 GB/s bandwidth enable larger batch sizes than RTX 2080's 616 GB/s GDDR6. Higher bandwidth minimizes data transfer bottlenecks during training, supporting batch sizes up to 50 percent larger on P100 for memory-bound workloads like large language models.

Power efficiency favors RTX 2080 at 215 W TDP versus P100's 250 W: it consumes less energy per TFLOP, ideal for prolonged inference serving. Both support NVLink interconnects, but P100's SXM2 form factor excels in multi-GPU clusters compared to RTX 2080's PCIe-only design.

The P100 excels in memory-constrained scenarios: its 16 GB HBM2 VRAM handles models exceeding 11 GB, such as fine-tuning large transformers with batch sizes leveraging 732 GB/s bandwidth. Datacenter users benefit from its SXM2 form factor and NVLink for scaled clusters at $0.07 per hour starting price.

The RTX 2080 suits budget-driven general compute: 10.1 TFLOPS FP16 outperforms P100's 9.3 TFLOPS at half the average cost of $0.10 per hour across eight providers. Lower 215 W TDP reduces operational expenses for inference or smaller training jobs fitting 8 to 11 GB VRAM.