A10 vs RTX 3090

The A10 achieves 125 TFLOPS dense FP16 compared to 71 TFLOPS dense FP16 on the RTX 3090 which translates to higher throughput for mixed precision training operations on the A10. In inference scenarios the A10 also supplies 250 TOPS dense INT8 versus 284 TOPS dense INT8 on the RTX 3090 indicating the RTX 3090 holds a modest advantage in integer quantized inference. The FP32 rating stands at 31.2 TFLOPS for the A10 and 35.6 TFLOPS for the RTX 3090 so the RTX 3090 processes single precision workloads at a higher rate. Memory bandwidth reaches 600 GB/s on the A10 and 936 GB/s on the RTX 3090 which allows the RTX 3090 to sustain larger batch sizes during training or inference when data movement limits performance. The sparsity feature doubles the FP16 rating to 250 TFLOPS on the A10 and to 142 TFLOPS on the RTX 3090 so sparse matrix operations benefit proportionally more on the A10. Power consumption differs substantially with the A10 at 150W TDP versus 350W TDP on the RTX 3090 which affects deployment density in power constrained environments.

The A10 suits environments where power efficiency matters because its 150W TDP enables higher server density than the 350W TDP of the RTX 3090. Workloads that exploit the 125 TFLOPS dense FP16 rating of the A10 achieve better performance per watt than on the RTX 3090 with its 71 TFLOPS dense FP16 rating.

The RTX 3090 fits applications that require the 936 GB/s memory bandwidth to handle larger effective batch sizes compared to the 600 GB/s bandwidth of the A10. Its 35.6 TFLOPS FP32 performance exceeds the 31.2 TFLOPS FP32 performance of the A10 for tasks that rely on single precision arithmetic.