MI300X vs RTX 4080 SUPER

The MI300X dominates in raw compute with 1307 TFLOPS FP16 and 163 TFLOPS FP32, far exceeding the RTX 4080 SUPER's 48.7 TFLOPS in both formats: this enables the MI300X to accelerate large-scale AI training where FP32 precision matters for stability, while its FP8 at 2614 TFLOPS suits ultra-efficient inference. The RTX 4080 SUPER's balanced FP16 and FP32 performance handles smaller models adequately but struggles with datasets exceeding 16 GB VRAM.

Memory specs highlight the gap: 192 GB HBM3 on the MI300X supports enormous batch sizes in training, preventing out-of-memory errors for models like large LLMs, whereas the RTX 4080 SUPER's 717 GB/s bandwidth and 16 GB limit it to modest batches. In real-world terms, the MI300X's 5300 GB/s bandwidth reduces data transfer bottlenecks, speeding up iterations by factors tied to its sevenfold bandwidth advantage.

Power efficiency tilts toward the RTX 4080 SUPER at 320W TDP, ideal for edge deployments, but the MI300X's OAM form factor and Infinity Fabric interconnect enable scalable clusters, outperforming in multi-GPU scientific simulations.

Select the MI300X for workloads demanding massive VRAM, such as training LLMs with billions of parameters: its 192 GB HBM3 handles datasets that crash on 16 GB cards. Datacenter environments benefit from its 5300 GB/s bandwidth and 1307 TFLOPS FP16, enabling larger batch sizes and faster convergence in AI research.

The RTX 4080 SUPER suits budget-conscious users running inference on models under 16 GB or Stable Diffusion tasks: its $0.17 per hour starting price and 48.7 TFLOPS FP16 deliver quick results at low cost. Gaming, prototyping, or fine-tuning small models favor its 320W efficiency and PCIe form factor for single-node setups.