GTX 1080 vs MI300X

The FP32 rating of 163.4 TFLOPS on the MI300X exceeds the 8.9 TFLOPS on the GTX 1080 by a wide margin. Dense FP16 performance reaches 1307.4 TFLOPS on the MI300X which supports larger batch sizes during training compared to the unpublished FP16 on the GTX 1080. Memory bandwidth of 5300 GB/s versus 320 GB/s further allows the MI300X to process data intensive operations without bottlenecks. For inference workloads the dense FP16 figure of 1307.4 TFLOPS on the MI300X enables efficient execution while sparsity options extend to 2614.9 TFLOPS. The GTX 1080 with its 180W TDP suits environments where power constraints limit deployment of the 750W MI300X. Batch size scaling benefits directly from the 192 GB VRAM on the MI300X against the 8-11 GB limit.

The GTX 1080 suits scenarios with strict power budgets due to its 180W TDP compared to 750W. Applications that fit in 8-11 GB VRAM and require only 8.9 TFLOPS FP32 avoid the overhead of larger systems. PCIe 3.0 interconnect matches legacy setups where Infinity Fabric is unnecessary.

The MI300X excels in large scale computations with 192 GB VRAM and 5300 GB/s bandwidth supporting models beyond 8-11 GB capacity. Dense FP16 at 1307.4 TFLOPS accelerates training relative to the GTX 1080 FP32 only rating. OAM form factor with PCIe 5.0 fits modern clusters needing high interconnect speeds.