A100 PCIe 40GB vs Tesla P100

The A100's FP16 performance of 312 TFLOPS vastly outpaces the P100's 9.3 TFLOPS, offering over 33 times the throughput for mixed-precision training common in deep learning. This disparity accelerates neural network training, where FP16 reduces memory usage and speeds iterations without significant accuracy loss. FP32 performance also favors the A100 at 19.5 TFLOPS versus 9.3 TFLOPS, benefiting single-precision scientific simulations and inference tasks.

Memory bandwidth of 2039 GB/s on the A100 supports larger batch sizes than the P100's 732 GB/s, minimizing data transfer bottlenecks and improving GPU utilization in memory-intensive workloads like large language model training. The A100's 40 GB VRAM capacity handles models exceeding 16 GB, preventing out-of-memory errors that plague the P100.

Power consumption reflects these gains: the A100 draws 400W TDP compared to 250W on the P100, indicating higher efficiency per watt in compute-heavy scenarios but requiring robust cooling in dense deployments.

The A100 PCIe 40GB excels in modern AI training and inference requiring high FP16 throughput of 312 TFLOPS and 40 GB VRAM. It suits workloads like large language models where memory bandwidth of 2039 GB/s enables efficient large-batch processing. Cloud users benefit from its PCIe 4.0 support and availability across 11 providers starting at $0.60 per hour.

Scenarios demanding Ampere tensor cores for accelerated matrix operations favor the A100 over Pascal-era limitations.

The Tesla P100 fits budget-constrained or legacy applications optimized for Pascal architecture, with average pricing of $0.60 per hour across its single offer. Its 250W TDP allows denser deployments than the A100's 400W, reducing power costs in clusters running FP32 workloads at 9.3 TFLOPS.

It serves environments with models under 16 GB HBM2 VRAM and bandwidth needs below 732 GB/s, avoiding upgrade expenses for compatible software.