A30 vs RTX 2080

The FP16 dense performance ratio of 165 TFLOPS on the A30 to 40.3 TFLOPS on the RTX 2080 indicates the A30 completes dense FP16 operations more than four times faster for training workloads that rely on this precision. The FP32 figures of 10.3 TFLOPS on the A30 and 10 TFLOPS on the RTX 2080 show nearly identical throughput for single precision scientific calculations. Memory bandwidth of 933 GB/s on the A30 versus 448 GB/s on the RTX 2080 allows the A30 to sustain larger batch sizes during inference without memory stalls. The A30 INT8 dense rating of 330 TOPS compared with 161.1 TOPS on the RTX 2080 further widens the gap for quantized inference paths that use dense INT8 arithmetic.

The A30 suits workloads that require 24 GB HBM2 memory and 933 GB/s bandwidth for large model inference at FP16 dense rates of 165 TFLOPS. Its 165W TDP and PCIe 4.0 interconnect also favor deployments that prioritize lower power draw alongside NVLink connectivity for multi GPU scaling.

The RTX 2080 fits scenarios limited to 8 to 11 GB GDDR6 memory where FP32 performance at 10 TFLOPS meets basic visualization or light compute needs at 215W TDP. Its availability in consumer form factors supports single GPU setups that do not require the higher memory capacity of the A30.