A16 vs RTX 5090

The FP16 dense figure of 17.9 TFLOPS on the A16 stands 4 times above its FP32 figure of 4.5 TFLOPS while the FP16 dense figure of 209.5 TFLOPS on the RTX 5090 stands 2 times above its FP32 figure of 104.8 TFLOPS. This ratio indicates the A16 allocates more relative throughput to reduced precision workloads than the RTX 5090. Memory bandwidth of 200 GB/s on the A16 versus 1792 GB/s on the RTX 5090 limits sustainable batch sizes on the A16 during both training and inference passes. Direct comparison of dense FP16 shows the RTX 5090 at 209.5 TFLOPS against the A16 at 17.9 TFLOPS. Direct comparison of FP16 with sparsity shows the RTX 5090 at 419 TFLOPS against the A16 at 35.9 TFLOPS. These bandwidth and throughput deltas translate into fewer simultaneous operations per second on the A16 across identical workloads.

The A16 suits deployments where TDP must remain at 250W and FP16 dense throughput of 17.9 TFLOPS meets requirements. Its 16 GB GDDR6 configuration and 200 GB/s bandwidth support inference tasks that do not demand the 32 GB capacity of newer cards.

The RTX 5090 suits deployments where FP16 dense throughput of 209.5 TFLOPS and memory bandwidth of 1792 GB/s enable larger batch processing. Its 32 GB GDDR7 configuration and 575W TDP accommodate training and inference workloads that exceed the limits of 16 GB and 200 GB/s hardware.