A40 vs Quadro P4000

The A40 FP32 figure of 37.4 TFLOPS exceeds the Quadro P4000 FP32 figure of 5.3 TFLOPS by a factor of seven. This difference means the A40 completes FP32 operations in scientific computing or rendering tasks in less time. The A40 dense FP16 figure of 149.7 TFLOPS supports training and inference workloads that benefit from half precision arithmetic. The Quadro P4000 lacks a published FP16 figure so direct comparison is not possible. Memory bandwidth of 696 GB/s on the A40 versus 243 GB/s on the Quadro P4000 allows the A40 to sustain larger batch sizes during model training without memory stalls. The A40 dense FP16 figure of 149.7 TFLOPS and the A40 figure with sparsity of 299.4 TFLOPS provide options for precision sensitive applications.

The A40 suits workloads that require 48 GB GDDR6 VRAM and 696 GB/s memory bandwidth. Training or inference jobs that use dense FP16 at 149.7 TFLOPS finish faster on the A40 than on GPUs with lower FP32 throughput of 5.3 TFLOPS.

The Quadro P4000 suits workloads that fit within 8 GB GDDR5 VRAM and require only 5.3 TFLOPS FP32. Applications that operate under a 105W TDP limit benefit from the lower power draw compared with 300W. Simple inference or visualization tasks that do not need published FP16 support run adequately on this GPU.