A40 vs RTX 2070

FP16 dense performance reaches 149.7 TFLOPS on the A40 compared with 29.9 TFLOPS dense FP16 on the RTX 2070. This fivefold difference supports substantially shorter iteration cycles for training workloads that rely on dense half precision arithmetic. The A40 also lists FP16 with sparsity at 299.4 TFLOPS while the RTX 2070 publishes no sparse FP16 value. Memory bandwidth of 696 GB/s on the A40 versus 448 GB/s on the RTX 2070 permits larger batch sizes before memory capacity limits are reached during both training and inference passes. INT8 dense throughput stands at 299.3 TOPS for the A40 and 119.4 TOPS for the RTX 2070 which produces corresponding gains in quantized inference throughput on the A40.

The A40 suits workloads that require 48 GB VRAM capacity and 696 GB/s bandwidth for large batch processing. Its 37.4 TFLOPS FP32 rating and NVLink interconnect further favor multi GPU scaling in professional environments that exceed the 8 GB limit of the RTX 2070.

The RTX 2070 fits lighter tasks that stay within 8 GB VRAM and operate at 175W TDP. Its 7.5 TFLOPS FP32 rating remains adequate for entry level inference or development work that does not demand the 48 GB capacity or 300W envelope of the A40.