MI300X vs T4

The MI300X vastly outperforms the T4 in compute capabilities: its 1307 TFLOPS FP16 and 163 TFLOPS FP32 eclipse the T4's matched 8.1 TFLOPS in both precisions. This disparity accelerates deep learning training, where FP16 dominates, enabling the MI300X to process models 161 times faster in FP16 tasks. For inference, the MI300X's 2614 TFLOPS FP8 further widens the gap, supporting quantized models at scales impossible on the T4.

Memory specifications define real-world usability: the MI300X's 192 GB HBM3 versus 16 GB GDDR6 allows batch sizes up to 12 times larger for memory-intensive models like 70B parameter LLMs. Bandwidth of 5300 GB/s on the MI300X versus 320 GB/s on the T4 reduces data bottlenecks, cutting training epochs by orders of magnitude. Power draw reflects this: 750W TDP for MI300X demands robust cooling, while T4's 70W suits efficient deployments.

These differences mean the MI300X excels in throughput-heavy scenarios, whereas the T4 limits users to smaller models with frequent swapping.

Select the MI300X for large-scale AI training and inference requiring immense VRAM. Its 192 GB HBM3 handles models exceeding 100B parameters without partitioning, and 5300 GB/s bandwidth supports massive batch sizes. At $0.50 per hour starting price, it delivers value for HPC clusters using Infinity Fabric and PCIe 5.0 interconnects.

Choose the T4 for cost-sensitive, low-power inference on modest models. Its 70W TDP enables dense deployments in edge or virtualized environments, with 16 GB GDDR6 suiting tasks under 7B parameters. Averaging $1.66 per hour, it provides economical scaling across PCIe form factors without high infrastructure costs.