RTX 2070 vs RTX 5080

Dense FP16 performance stands at 29.9 TFLOPS on the RTX 2070 versus 112.6 TFLOPS on the RTX 5080 producing a ratio of nearly four times. The FP16 to FP32 ratio equals four on the RTX 2070 and two on the RTX 5080 indicating different efficiency profiles for mixed precision workloads. In training scenarios the higher dense FP16 figure on the RTX 5080 supports larger model updates per step while the RTX 2070 remains constrained by its 29.9 TFLOPS dense FP16 ceiling. Memory bandwidth of 960 GB per second on the RTX 5080 versus 448 GB per second on the RTX 2070 permits roughly double the data movement per second allowing larger batch sizes during inference before memory saturation occurs. Dense INT8 throughput reaches 119.4 TOPS on the RTX 2070 and 450.2 TOPS on the RTX 5080 further widening the gap for quantized inference paths.

The RTX 2070 suits environments where power draw must stay at 175 W and PCIe 3.0 interconnects are the only available option. Workloads that fit within 8 GB VRAM and require only 29.9 TFLOPS dense FP16 avoid the higher 360 W TDP of newer cards while still delivering adequate throughput for modest scientific computing tasks.

The RTX 5080 fits deployments that demand 16 GB VRAM and 112.6 TFLOPS dense FP16 to handle larger batches or models. Its 960 GB per second bandwidth and 56.3 TFLOPS FP32 rating accelerate fine tuning sessions that exceed the limits of the RTX 2070 specifications.