MI325X vs RTX 2080 Ti

Memory specifications define key real-world differences: the MI325X's 256 GB HBM3e VRAM supports massive batch sizes and large models in training, while the RTX 2080 Ti's 11 GB GDDR6 limits it to smaller datasets or heavy quantization. The MI325X's 6000 GB/s bandwidth enables high-throughput data feeds for inference and training, compared to 616 GB/s on the RTX 2080 Ti, which bottlenecks memory-bound tasks like large-batch processing.

Compute performance underscores the gap in AI suitability. Both GPUs offer equal FP16 and FP32 rates at 1307 TFLOPS for the MI325X versus 10.1 TFLOPS for the RTX 2080 Ti, meaning the MI325X accelerates training and inference over 129 times faster in these precisions. The MI325X's additional 2614 TFLOPS FP8 capability boosts low-precision inference efficiency. Power draw of 750W on the MI325X powers dense compute, whereas 215W on the RTX 2080 Ti fits edge or desktop scenarios.

The MI325X is the superior choice for large-scale AI and HPC workloads: its 256 GB HBM3e VRAM and 6000 GB/s bandwidth handle enormous models and datasets in LLM training or scientific simulations. Infinity Fabric interconnect optimizes multi-GPU scaling in datacenter OAM deployments with 1307 TFLOPS FP16 performance.

The RTX 2080 Ti suits budget-conscious users for prototyping or gaming: cloud pricing starts at $0.06 per hour, making it viable for light inference or Stable Diffusion on 11 GB VRAM. Its 215W TDP and PCIe form factor enable easy integration in desktops or small-scale NVLink setups with 10.1 TFLOPS FP32.