RTX 4070 vs RTX 4080

The FP16 dense to FP32 ratio equals 2.0 on both cards since 58.3 divided by 29.1 matches 97.5 divided by 48.7. This ratio means mixed precision training can double effective throughput compared to pure FP32 operations. Inference workloads gain similar acceleration when using dense FP16 calculations. Memory bandwidth differences of 504 GB per second against 717 GB per second allow the RTX 4080 to sustain larger batch sizes during training sessions that process extensive datasets. When sparsity is enabled the FP16 figures become 116.6 TFLOPS and 195 TFLOPS. These sparse values maintain the same proportional advantage for the RTX 4080 in applicable sparse matrix operations. The FP8 dense performance of 116.6 TFLOPS versus 194.9 TFLOPS follows an identical pattern for lower precision inference paths.

The RTX 4070 suits scenarios where power efficiency matters with its 200 W TDP. Tasks that require no more than 12 to 16 GB VRAM benefit from this option without excess capacity. Dense FP16 performance at 58.3 TFLOPS handles moderate inference loads effectively while keeping energy draw lower than the 320 W alternative.

The RTX 4080 fits workloads that demand higher throughput with 97.5 TFLOPS dense FP16 and 717 GB per second bandwidth. Larger batch sizes become feasible during training due to the increased memory bandwidth over the 504 GB per second figure. Its 16 GB VRAM configuration supports models that exceed the lower capacity range of the RTX 4070.