MI250X vs RTX 2080

Dense FP16 performance reaches 383 TFLOPS on the MI250X compared with 40.3 TFLOPS dense FP16 on the RTX 2080 which produces a ratio of roughly nine times greater throughput for matrix operations in training and inference. The same ratio applies to dense INT8 figures of 383 TOPS versus 161.1 TOPS. FP32 throughput stands at 47.9 TFLOPS on the MI250X against 10 TFLOPS on the RTX 2080. Memory bandwidth of 3277 GB/s on the MI250X versus 448 GB/s on the RTX 2080 permits substantially larger batch sizes before data movement becomes the limiter in either training or inference pipelines.

The MI250X suits workloads that require the full 128 GB HBM2e capacity together with 383 TFLOPS dense FP16 or 47.9 TFLOPS FP32. Scientific computing jobs and large model training benefit directly from the 3277 GB/s bandwidth and Infinity Fabric interconnect when scaling across multiple OAM modules exceeds the limits of an 8 to 11 GB device.

The RTX 2080 fits tasks constrained by a 215W power envelope and a standard PCIe slot. Inference or fine tuning of models that fit inside 8 to 11 GB GDDR6 memory can proceed at 40.3 TFLOPS dense FP16 without the infrastructure overhead of an OAM form factor or 560W supply.