A100 vs L40

The A100 dense FP16 performance of 312 TFLOPS exceeds the L40 dense FP16 performance of 181.05 TFLOPS by a factor derived directly from those figures. This difference means the A100 supports larger batch sizes during training workloads that rely on dense FP16 operations. Memory bandwidth of 2039 GB/s on the A100 versus 864 GB/s on the L40 further enables the A100 to sustain higher throughput when data movement limits model scale. In contrast the L40 dense FP32 figure of 90.5 TFLOPS surpasses the A100 dense FP32 figure of 19.5 TFLOPS so inference or compute tasks that favor FP32 see an advantage on the L40. Sparsity comparisons follow the same pattern with the A100 at 624 TFLOPS dense FP16 sparsity versus the L40 at 362.1 TFLOPS dense FP16 sparsity.

The A100 suits workloads that require the 2039 GB/s memory bandwidth together with 312 TFLOPS dense FP16. Large scale training jobs benefit from the combination of 40-80 GB HBM2e capacity and NVLink interconnect that the L40 lacks. The 624 TOPS dense INT8 rating also favors the A100 for quantized inference pipelines that stay within dense operations.

The L40 suits workloads that require the 90.5 TFLOPS FP32 rating together with 300W TDP. Inference tasks that run on dense FP32 operations gain from this specification while the lower power draw reduces overall system requirements compared to the 400W TDP of the A100. The 48 GB GDDR6 memory remains adequate for models that fit within that capacity without needing the higher bandwidth of the A100.