RTX 2080 vs RTX 4090

Dense FP16 performance reaches 40.3 TFLOPS on the RTX 2080 and 165.2 TFLOPS on the RTX 4090 while FP32 performance reaches 10 TFLOPS on the RTX 2080 and 82.6 TFLOPS on the RTX 4090. The larger dense FP16 to FP32 ratio on the RTX 4090 supports faster matrix operations during both training and inference phases. Memory bandwidth of 1008 GB per second on the RTX 4090 versus 448 GB per second on the RTX 2080 permits larger batch sizes before memory capacity limits are reached. Dense INT8 performance stands at 161.1 TOPS on the RTX 2080 and 660.6 TOPS on the RTX 4090 while dense FP8 performance stands at 330.3 TFLOPS on the RTX 4090.

The RTX 2080 suits environments where TDP must remain at 215 W and where PCIe 3.0 interconnect matches existing hardware. Its 8 to 11 GB GDDR6 VRAM handles smaller models that fit within that capacity without requiring the 450 W power draw of newer cards. Systems already equipped with NVLink benefit from continued use of the RTX 2080 without platform changes.

The RTX 4090 suits workloads that require 24 GB GDDR6X VRAM and 1008 GB per second memory bandwidth to accommodate larger batches or models. Its dense FP16 figure of 165.2 TFLOPS and dense FP32 figure of 82.6 TFLOPS accelerate training and inference relative to the RTX 2080. PCIe 4.0 interconnect further supports higher throughput on compatible motherboards.