MI355X vs RTX 3080 Ti

The MI355X outperforms the RTX 3080 Ti dramatically in raw compute: 2300 TFLOPS FP16 and FP32 compared to 29.8 TFLOPS, translating to roughly 77 times higher throughput for AI training and inference. This delta means the MI355X accelerates large-scale model training by processing vast datasets in fractions of the time the RTX 3080 Ti requires. For inference, the FP8 capability at 4600 TFLOPS on the MI355X supports ultra-efficient serving of massive LLMs. Memory differences are stark: 288 GB HBM3e at 8000 GB/s versus 10 to 12 GB GDDR6X at 760 GB/s allows the MI355X to handle enormous batch sizes without swapping, ideal for training models over 100 billion parameters. The RTX 3080 Ti struggles with memory-bound tasks, limiting batch sizes to small values and increasing latency. Power draw reflects this: 750W for MI355X versus 320W, suiting datacenter cooling over desktop setups.

Select the MI355X for datacenter AI workloads requiring extreme scale, such as training LLMs with hundreds of billions of parameters. Its 288 GB HBM3e VRAM and 8000 GB/s bandwidth enable massive batch sizes unattainable on the RTX 3080 Ti's 10 to 12 GB. The 2300 TFLOPS FP16 performance suits high-throughput scientific simulations and inference at scale.

Choose the RTX 3080 Ti for budget-conscious, smaller-scale tasks like gaming or lightweight AI inference, available from $0.08 per hour. Its 320W TDP fits consumer or edge deployments, and 29.8 TFLOPS FP32 suffices for Stable Diffusion or fine-tuning modest models. No live offers exist for MI355X, making RTX 3080 Ti immediately accessible.