MI300X vs RTX 4090

The MI300X records 1307.4 TFLOPS dense FP16 against the RTX 4090 165.2 TFLOPS dense FP16 while both devices report FP32 figures of 163.4 TFLOPS and 82.6 TFLOPS respectively. This dense FP16 advantage allows the MI300X to process larger batches during training and inference workloads that fit within its 192 GB memory capacity. The 5300 GB/s bandwidth on the MI300X compared with 1008 GB/s on the RTX 4090 further supports sustained data movement for models that exceed the 24 GB limit of the RTX 4090. When sparsity is enabled the MI300X reaches 2614.9 TFLOPS with sparsity FP16 against the RTX 4090 330.4 TFLOPS with sparsity FP16.

The MI300X suits deployments that require the full 192 GB HBM3 capacity and 5300 GB/s bandwidth for models that cannot fit on smaller memory configurations. Its 1307.4 TFLOPS dense FP16 and 2614.9 TFLOPS with sparsity FP16 also accelerate large batch training sessions that benefit from the 750W TDP envelope.

The RTX 4090 fits environments constrained to 450W TDP and PCIe form factor cards where the 24 GB GDDR6X memory suffices for the target workload. Its 165.2 TFLOPS dense FP16 remains adequate for compact inference tasks that do not demand the higher memory bandwidth of 5300 GB/s.