A100 SXM4 40GB vs Tesla P100

The A100's FP16 performance of 312 TFLOPS vastly outpaces the P100's 9.3 TFLOPS, accelerating mixed-precision training in deep learning models by over 33 times in theoretical throughput. This delta proves critical for training large neural networks, where FP16 reduces memory usage and speeds iterations without sacrificing accuracy via techniques like automatic mixed precision. Inference workloads similarly benefit, as the A100 handles more simultaneous queries efficiently.

FP32 capabilities further favor the A100 at 19.5 TFLOPS over the P100's 9.3 TFLOPS, suiting scientific simulations or single-precision tasks requiring twice the compute. Memory bandwidth of 2039 GB/s on the A100 supports larger batch sizes in training, minimizing overhead from data loading compared to the P100's 732 GB/s limitation, which constrains models to smaller batches and longer runtimes. The A100's 40 GB VRAM accommodates massive models directly, avoiding multi-GPU complexity inherent to the P100's 16 GB.

Higher TDP of 400W on the A100 correlates with sustained performance under load, while the P100's 250W suits lighter duties but throttles in intensive scenarios.

Opt for the A100 SXM4 40GB in demanding AI training or inference pipelines handling large language models or high-resolution generative tasks. Its 312 TFLOPS FP16 and 40 GB VRAM enable processing datasets that exceed the P100's 16 GB capacity, reducing training times significantly. Cloud users prioritizing speed over initial cost find value in its availability across five providers from $1.00 per hour.

Select the P100 for budget-constrained legacy applications or basic inference on smaller models fitting within 16 GB VRAM. At $0.60 per hour, it offers economical entry for Pascal-compatible codebases without needing architectural migrations. Its 250W TDP and NVLink interconnect suit compact, low-power clusters for prototyping or non-intensive scientific computing.