A16 vs T4

The FP16 dense figure of 65 TFLOPS on the T4 exceeds the 17.9 TFLOPS dense figure on the A16 by a ratio of roughly 3.6 to 1, which indicates faster dense FP16 inference workloads on the T4. The FP32 figure of 8.1 TFLOPS on the T4 likewise exceeds the 4.5 TFLOPS on the A16, suggesting advantages for certain training steps that rely on dense FP32 arithmetic. Memory bandwidth of 320 GB/s on the T4 exceeds 200 GB/s on the A16 and therefore supports larger batch sizes before memory becomes the limiter. The A16 provides an FP16 with sparsity figure of 35.9 TFLOPS that has no corresponding published value on the T4, so sparse FP16 comparisons remain unavailable. INT8 dense performance of 130 TOPS on the T4 exceeds the 35.9 TOPS dense figure on the A16.

The A16 suits deployments that require PCIe 4.0 interconnect together with access to sparsity acceleration. Its INT8 with sparsity rating of 71.8 TOPS provides a published path for workloads that can exploit sparsity. The 250W TDP remains acceptable when power budgets allow newer architecture features.

The T4 suits power constrained environments because its TDP of 70W is substantially lower than 250W. Its dense FP16 rating of 65 TFLOPS and dense INT8 rating of 130 TOPS deliver higher throughput numbers for dense tensor operations. The 320 GB/s memory bandwidth further aids batch size scaling in inference scenarios.