A100 vs RTX 5080

The A100 delivers 312 TFLOPS dense FP16 compared with 112.6 TFLOPS dense FP16 on the RTX 5080, which means the A100 processes larger matrix operations in training at a higher rate when sparsity is not applied. In inference the same dense FP16 advantage allows the A100 to handle bigger batch sizes supported by its 2039 GB per second bandwidth versus the 960 GB per second bandwidth of the RTX 5080. The RTX 5080 provides 56.3 TFLOPS FP32 against the A100 19.5 TFLOPS FP32, so single precision workloads run faster on the RTX 5080. When sparsity is enabled the A100 reaches 624 TFLOPS FP16 while the RTX 5080 reaches 225.1 TFLOPS FP16, preserving the same relative ordering for sparse tensor operations.

The A100 suits large scale training runs that require 40 to 80 GB of memory and 2039 GB per second bandwidth to accommodate models exceeding 16 GB. Its 312 TFLOPS dense FP16 and 624 TOPS dense INT8 support high throughput matrix multiplications in data center environments where power draw of 400 watts is acceptable.

Its PCIe 5.0 interconnect and lower TDP make it suitable for compact deployments where 16 GB of memory suffices for smaller batch inference.