P100 vs RTX 5080

The RTX 5080 outperforms the P100 significantly in compute throughput: 56.3 TFLOPS FP16 and FP32 versus 9.3 TFLOPS, enabling up to six times faster model training and inference for deep learning workloads. This delta translates to reduced epoch times in training large language models, where FP16 precision accelerates matrix multiplications without accuracy loss.

Memory bandwidth impacts data transfer efficiency: the RTX 5080's 960 GB/s supports larger batch sizes than the P100's 732 GB/s, minimizing bottlenecks in inference pipelines handling high-resolution inputs. For example, Stable Diffusion tasks benefit from sustained throughput on the newer GPU, avoiding stalls during token generation.

Blackwell architecture in the RTX 5080 incorporates advancements over Pascal, enhancing tensor core efficiency despite higher 360W TDP compared to 250W. In real-world scenarios, this yields superior scaling for multi-GPU scientific simulations, where the P100's NVLink aids older clusters but cannot match raw speed.

Opt for the P100 in cost-sensitive environments requiring 16 GB VRAM at minimal expense, with pricing from $0.07 per hour. Its 250W TDP suits power-constrained deployments, and NVLink interconnect supports legacy HPC clusters running FP32 workloads at 9.3 TFLOPS. Basic inference or prototyping benefits from average $0.25 per hour rates across three providers.

Choose the RTX 5080 for demanding AI tasks leveraging 56.3 TFLOPS FP16 performance and 960 GB/s bandwidth, ideal for rapid LLM training despite $0.25 per hour starting price. Modern PCIe form factor fits current cloud instances, outperforming the P100 by six times in throughput-intensive applications like fine-tuning.