A40 vs RTX 5070

The A40 achieves 149.7 TFLOPS in dense FP16 which exceeds the 61.7 TFLOPS dense FP16 of the RTX 5070 by a factor of 2.4 and supports larger batch sizes during training or inference workloads that rely on dense operations. In sparse FP16 the A40 reaches 299.4 TFLOPS against 123.5 TFLOPS on the RTX 5070 maintaining the same ratio for sparsity enabled tasks. Memory bandwidth of 696 GB per second on the A40 versus 672 GB per second on the RTX 5070 allows the A40 to sustain higher throughput when moving data for models that exceed the 12 to 16 GB capacity of the RTX 5070. The FP32 performance of 37.4 TFLOPS on the A40 compared to 30.9 TFLOPS on the RTX 5070 further favors the A40 for precision sensitive scientific workloads that require dense FP32 calculations.

The A40 suits workloads that demand 48 GB of memory such as large model training or fine tuning where the 149.7 TFLOPS dense FP16 and NVLink interconnect provide advantages over the RTX 5070. Its 696 GB per second bandwidth and 299.3 TOPS dense INT8 also support inference scenarios that benefit from higher capacity and established Ampere architecture features.

The RTX 5070 fits power constrained environments with its 250W TDP and supports newer formats including 123.5 TFLOPS dense FP8 that the A40 does not publish. Its PCIe 5.0 interconnect and Blackwell architecture make it preferable for lighter inference tasks that fit within 12 to 16 GB and prioritize lower power draw over raw memory size.