MI300X vs RTX 2080 Ti

The MI300X vastly outpaces the RTX 2080 Ti in AI-relevant compute: 1307 TFLOPS FP16 supports accelerated training of deep neural networks, where half-precision computations reduce memory use without sacrificing accuracy. Its FP32 performance of 163 TFLOPS exceeds the RTX 2080 Ti's 10.1 TFLOPS, benefiting general-purpose simulations requiring single-precision arithmetic.

Memory specifications transform real-world workloads. The MI300X's 5300 GB/s bandwidth and 192 GB HBM3 VRAM enable massive batch sizes in model training, fitting billion-parameter LLMs entirely on one GPU and minimizing data transfer bottlenecks. The RTX 2080 Ti's 616 GB/s and 8-11 GB GDDR6 limit it to smaller batches, often necessitating gradient accumulation or multi-GPU setups.

Power and form factors reflect deployment scales: MI300X at 750W in OAM with Infinity Fabric and PCIe 5.0 suits clustered supercomputing, while RTX 2080 Ti's 215W PCIe design fits desktops but struggles with sustained high-throughput inference.

The MI300X excels in large-scale AI training and inference where VRAM exceeds 100 GB is essential. Its 192 GB HBM3 handles full loading of models like 70B-parameter LLMs, paired with 1307 TFLOPS FP16 for rapid iterations. High bandwidth of 5300 GB/s supports enormous batch sizes, ideal for research labs or enterprises optimizing cloud spend at $2.63/hr average.

Scientific computing benefits from 163 TFLOPS FP32 and Infinity Fabric interconnects for multi-GPU scaling.

The RTX 2080 Ti suits budget-conscious users prototyping small ML models or running Stable Diffusion. At $0.06/hr from cloud providers, its 10.1 TFLOPS FP16 and 11 GB VRAM manage fine-tuning up to 7B models or gaming workloads efficiently. Low 215W TDP enables easy local or edge deployment without datacenter cooling.