P100 vs RTX 3080

Compute performance favors the RTX 3080 decisively: its 29.8 TFLOPS in FP16 and FP32 enables training times reduced by approximately three times relative to the P100's 9.3 TFLOPS, accelerating deep learning model convergence. For inference, this translates to higher queries per second in production deployments. The matched FP16 and FP32 rates on both GPUs support mixed-precision training without penalties, but the RTX 3080's Ampere tensor cores enhance efficiency further.

VRAM capacity gives the P100 an edge at 16 GB HBM2 over the RTX 3080's 10-12 GB GDDR6X, allowing larger batch sizes in memory-constrained scenarios like fine-tuning large language models. Bandwidth differences are marginal at 732 GB/s versus 760 GB/s, minimally affecting data transfer in most workloads. The P100's NVLink interconnect facilitates multi-GPU scaling absent on the PCIe-only RTX 3080, though higher 320W TDP on the latter demands robust cooling.

Overall, newer architecture drives real-world speedups in compute-bound tasks, while P100 suits VRAM-intensive applications.

Select the P100 for workloads requiring 16 GB HBM2 VRAM to accommodate large models or batches without out-of-memory errors, such as certain scientific simulations. Its NVLink interconnect enables efficient multi-GPU communication in PCIe or SXM2 form factors, ideal for legacy Pascal-optimized HPC codes. Starting at $0.07/hr, it fits tight budgets where compute demands do not exceed 9.3 TFLOPS.

The RTX 3080 excels in modern compute-heavy tasks leveraging 29.8 TFLOPS FP16/FP32 for rapid LLM training or inference. Ampere architecture supports advanced features like improved tensor performance, outperforming Pascal in generative AI. With pricing from $0.06/hr averaging $0.15/hr across 10 offers, it provides better value and availability in PCIe form factors.