A40 vs RTX 5080

Dense FP16 performance on the A40 reaches 149.7 TFLOPS compared to 112.6 TFLOPS on the RTX 5080. This gap means the A40 can process larger batches during training operations that rely on dense FP16 calculations. The RTX 5080 shows 56.3 TFLOPS of FP32 performance against 37.4 TFLOPS on the A40 so inference tasks that use FP32 may complete faster on the RTX 5080. Memory bandwidth of 960 GB per second on the RTX 5080 exceeds the 696 GB per second on the A40 which supports larger batch sizes in bandwidth limited scenarios. Sparse FP16 performance measures 299.4 TFLOPS on the A40 versus 225.1 TFLOPS on the RTX 5080 so workloads that activate sparsity features retain an advantage on the A40.

The A40 suits workloads that require 48 GB of memory capacity for large models that exceed 16 GB limits. Dense FP16 performance at 149.7 TFLOPS combined with NVLink interconnect supports multi GPU training configurations where memory pooling matters. Scientific computing tasks that use 37.4 TFLOPS of FP32 and 299.3 TOPS of dense INT8 benefit from the 300 W TDP efficiency.

The RTX 5080 fits scenarios that prioritize 960 GB per second memory bandwidth and 56.3 TFLOPS of FP32 performance for faster single GPU inference. Newer PCIe 5.0 interconnect and FP8 dense performance at 225.1 TFLOPS enable efficient low precision inference pipelines that fit within 16 GB of memory.