A100 vs RTX 2080

The FP16 dense performance of 312 TFLOPS on the A100 exceeds the 40.3 TFLOPS FP16 dense performance on the RTX 2080 by a factor derived directly from these figures. This delta allows the A100 to complete training iterations faster than the RTX 2080 when both operate in dense FP16 mode. The A100 also provides 624 TOPS INT8 dense performance against 161.1 TOPS INT8 dense performance on the RTX 2080. Memory bandwidth of 2039 GB/s on the A100 supports larger batch sizes than the 448 GB/s bandwidth on the RTX 2080 during inference workloads. FP32 performance reaches 19.5 TFLOPS on the A100 compared to 10 TFLOPS on the RTX 2080. The 400W TDP of the A100 exceeds the 215W TDP of the RTX 2080 yet enables higher throughput in sustained operations.

The A100 serves workloads that require 40 to 80 GB HBM2e memory and 2039 GB/s bandwidth for processing large datasets. Its 312 TFLOPS FP16 dense performance and 624 TFLOPS FP16 with sparsity support extended training sessions that exceed the capacity of 8 to 11 GB GDDR6 memory. The A100 fits environments that utilize NVLink and InfiniBand interconnects for multi GPU scaling.

The RTX 2080 fits tasks limited to 8 to 11 GB GDDR6 memory and 40.3 TFLOPS FP16 dense performance. Its 215W TDP allows deployment in systems with lower power constraints than the 400W TDP of the A100. The RTX 2080 handles inference at 161.1 TOPS INT8 dense performance for smaller models that do not require HBM2e capacity.