MI300X vs RTX 3080 Ti

The MI300X dominates in compute-intensive tasks due to its FP16 rating of 1307 TFLOPS, which is 43.8 times higher than the RTX 3080 Ti's 29.8 TFLOPS: this gap accelerates AI training and inference where half-precision arithmetic prevails. Its FP32 performance of 163 TFLOPS still outpaces the RTX 3080 Ti's 29.8 TFLOPS by 5.5 times, benefiting general-purpose computing. Memory differences prove critical: 192 GB HBM3 versus 10-12 GB GDDR6X allows the MI300X to process models with billions of parameters without splitting, while the RTX 3080 Ti limits users to smaller batches or quantized models. Bandwidth of 5300 GB/s on the MI300X, 7 times the RTX 3080 Ti's 760 GB/s, reduces bottlenecks in data-heavy workloads like large language model training, enabling larger batch sizes and faster iterations. Power draw reflects this: 750W TDP for MI300X versus 320W for RTX 3080 Ti signals datacenter efficiency at scale.

Users should select the MI300X for large-scale AI training or inference involving models exceeding 10-12 GB VRAM, such as full-parameter fine-tuning of LLMs. Its 192 GB HBM3 and 5300 GB/s bandwidth support massive batch sizes, while 1307 TFLOPS FP16 delivers rapid throughput. Datacenter features like Infinity Fabric and PCIe 5.0 suit multi-GPU clusters at $0.50/hr starting price.

The RTX 3080 Ti fits budget-conscious prototyping, gaming, or small-scale inference where 10-12 GB GDDR6X suffices. At $0.08/hr average $0.14/hr, it offers strong value for Stable Diffusion or fine-tuning compact models with 29.8 TFLOPS FP16. Its 320W TDP and PCIe form factor enable easy deployment in consumer cloud instances.