A16 vs RTX 2080

The FP16 dense to FP32 ratio equals 17.9 TFLOPS divided by 4.5 TFLOPS on the A16 and 40.3 TFLOPS divided by 10 TFLOPS on the RTX 2080. This ratio indicates similar scaling for mixed precision operations in training workloads. The RTX 2080 delivers 40.3 TFLOPS FP16 dense compared to 17.9 TFLOPS FP16 dense on the A16 which supports larger batch sizes during inference. Memory bandwidth of 448 GB/s on the RTX 2080 versus 200 GB/s on the A16 further enables higher throughput for data movement in dense FP16 tasks. The A16 lists INT8 dense at 35.9 TOPS while the RTX 2080 lists INT8 dense at 161.1 TOPS. Sparsity options on the A16 reach 35.9 TFLOPS FP16 with sparsity and 71.8 TOPS INT8 with sparsity yet no equivalent sparsity figures appear for the RTX 2080.

The A16 suits scenarios that require 16 GB GDDR6 VRAM for models exceeding 8-11 GB capacity. Its PCIe 4.0 interconnect and 250W TDP fit deployments where higher memory capacity takes priority over peak dense FP16 throughput of 17.9 TFLOPS. Sparsity support at 35.9 TFLOPS FP16 with sparsity provides an option for workloads that can utilize structured pruning.

The RTX 2080 suits scenarios that require 40.3 TFLOPS FP16 dense or 10 TFLOPS FP32 along with 448 GB/s memory bandwidth. Its 215W TDP and NVLink interconnect fit environments where higher dense tensor performance and faster data transfer matter more than the 16 GB VRAM on the A16.