GTX 1080 Ti vs MI355X

Compute throughput defines the core disparity: the MI355X achieves 2300 TFLOPS in FP16 and FP32, over 258 times the GTX 1080 Ti's 8.9 TFLOPS, accelerating neural network training and inference dramatically. Equal FP16 and FP32 rates on both indicate no precision penalty, but the MI355X's FP8 at 4600 TFLOPS further boosts low-precision inference efficiency.

Memory specs transform real-world usage. The MI355X's 288 GB VRAM handles enormous models without quantization, unlike the GTX 1080 Ti's 8 to 11 GB limit that restricts batch sizes in large LLMs. Its 8000 GB/s bandwidth versus 320 GB/s minimizes stalls, enabling 25 times larger batches for faster training convergence and higher inference throughput. TDP contrasts at 750 W for MI355X and 180 W for GTX 1080 Ti, impacting power budgets.

The GTX 1080 Ti fits budget-constrained, low-demand scenarios like prototyping small ML models or legacy software. Its 180 W TDP and $0.60 per hour pricing enable cost-effective runs on PCIe instances where 8.9 TFLOPS FP32 and up to 11 GB VRAM suffice, avoiding overkill for non-memory-intensive tasks.

The MI355X excels in high-scale AI and HPC, leveraging 2300 TFLOPS FP16/FP32 and 288 GB HBM3e for training massive LLMs or complex simulations. Infinity Fabric interconnect and OAM form factor support multi-GPU clusters, with 8000 GB/s bandwidth optimizing large-batch inference.