Tesla P100 vs RTX 2080 Ti

The P100's 16 GB HBM2 VRAM exceeds the RTX 2080 Ti's 8-11 GB GDDR6, enabling larger batch sizes in training scenarios where models exceed 11 GB. Higher memory bandwidth of 732 GB/s on the P100 versus 616 GB/s on the RTX 2080 Ti reduces data transfer bottlenecks, supporting faster iterations in memory-intensive deep learning.

FP16 and FP32 performance shows the RTX 2080 Ti at 10.1 TFLOPS each slightly ahead of the P100's 9.3 TFLOPS, benefiting inference tasks with half-precision computations common in deployment. Equal FP16 to FP32 ratios on both indicate balanced scalar performance without specialized tensor core boosts in these specs. The P100's 250W TDP contrasts the RTX 2080 Ti's 215W, implying higher power draw for sustained datacenter loads versus efficient consumer use.

In real-world terms, P100 excels in large-model training with bigger batches due to VRAM and bandwidth advantages, while RTX 2080 Ti offers marginal compute edge for lighter inference at lower cost.

Choose the P100 for workloads demanding over 11 GB VRAM, such as training large language models where 16 GB HBM2 prevents out-of-memory errors. Its 732 GB/s bandwidth handles high-throughput data movement effectively in scientific computing or fine-tuning with massive datasets. NVLink interconnect aids multi-GPU scaling in SXM2 setups.

Opt for the RTX 2080 Ti in cost-sensitive inference or Stable Diffusion tasks, with pricing from $0.06 per hour versus P100's $0.60 per hour. The 10.1 TFLOPS FP16 performance suits real-time deployment, and 215W TDP fits power-constrained clouds. Turing architecture provides modern features for gaming-adjacent ML like image generation.