RTX 3090 vs RTX 5080

Dense FP16 performance reaches 71 TFLOPS on the RTX 3090 and 112.6 TFLOPS on the RTX 5080 while sparse FP16 performance reaches 142 TFLOPS on the RTX 3090 and 225.1 TFLOPS on the RTX 5080. The higher dense FP16 figure on the RTX 5080 supports faster training iterations and inference throughput for models that stay within its 16 GB limit compared to the RTX 3090. FP32 performance of 56.3 TFLOPS on the RTX 5080 versus 35.6 TFLOPS on the RTX 3090 indicates stronger single precision scaling for scientific workloads. Memory bandwidth of 960 GB/s on the RTX 5080 versus 936 GB/s on the RTX 3090 allows marginally larger batch sizes in memory constrained scenarios though the RTX 3090 24 GB VRAM enables larger models overall. INT8 dense performance of 450.2 TOPS on the RTX 5080 exceeds the 284 TOPS on the RTX 3090 for quantized inference paths.

The RTX 3090 suits workloads that require its 24 GB GDDR6X VRAM capacity such as fine tuning larger models that exceed 16 GB. Its 71 TFLOPS dense FP16 rating combined with NVLink interconnect supports multi GPU setups where memory pooling matters more than peak throughput per card. The 350W TDP also fits power constrained older server deployments that cannot accommodate the 360W rating of the RTX 5080.

The RTX 5080 fits inference and training tasks that benefit from its 112.6 TFLOPS dense FP16 rating and 960 GB/s memory bandwidth within the 16 GB GDDR7 limit. PCIe 5.0 interconnect provides higher host transfer rates than the PCIe 4.0 on the RTX 3090 for data intensive pipelines. Higher dense INT8 performance of 450.2 TOPS accelerates quantized inference compared to the 284 TOPS on the RTX 3090.