MI300X vs RTX 3090

Dense FP16 performance on the MI300X reaches 1307.4 TFLOPS while the RTX 3090 delivers 71 TFLOPS. This ratio supports faster training iterations and larger model inference on the MI300X for operations that rely on dense FP16 arithmetic. Sparse FP16 performance stands at 2614.9 TFLOPS on the MI300X against 142 TFLOPS on the RTX 3090 and follows the same pattern for workloads that exploit sparsity. Memory bandwidth of 5300 GB/s on the MI300X exceeds 936 GB/s on the RTX 3090 by a factor derived directly from these figures. Higher bandwidth permits larger batch sizes during training and inference without memory stalls. FP32 throughput of 163.4 TFLOPS on the MI300X compared to 35.6 TFLOPS on the RTX 3090 further widens the gap for precision sensitive scientific tasks. INT8 dense performance measures 2614.9 TOPS on the MI300X versus 284 TOPS on the RTX 3090. The MI300X TDP of 750W versus 350W on the RTX 3090 indicates different power envelopes that influence sustained performance in dense versus sparse modes.

The MI300X suits scenarios that require 192 GB VRAM for models exceeding the 24 GB capacity of the RTX 3090. Dense FP16 throughput of 1307.4 TFLOPS enables accelerated training of large language models where memory capacity limits batch sizes on smaller GPUs. Infinity Fabric interconnect supports multi GPU scaling in CDNA 3 based clusters.

The RTX 3090 fits deployments constrained to 350W TDP and PCIe form factors. FP32 performance of 35.6 TFLOPS combined with 24 GB GDDR6X VRAM supports consumer grade inference and fine tuning tasks that remain within these limits. NVLink interconnect allows pairing in systems that already use PCIe 4.0 infrastructure.