H100 SXM5 vs RTX 3080 Ti

The H100 SXM5 excels in AI due to its FP16 performance of 1979 TFLOPS, far exceeding FP32 at 67 TFLOPS, which accelerates training and inference for models using half-precision arithmetic. The RTX 3080 Ti matches FP16 and FP32 at 29.8 TFLOPS each, providing balanced compute for graphics or mixed workloads but lacking the H100 SXM5's AI specialization.

Memory bandwidth defines practical limits: the H100 SXM5's 3350 GB/s enables large batch sizes in deep learning, fitting models up to 94 GB without swapping. The RTX 3080 Ti's 760 GB/s restricts it to smaller batches and models under 12 GB, often requiring gradient accumulation or model parallelism. Power draw of 700W on the H100 SXM5 versus 320W on the RTX 3080 Ti influences deployment density in clusters.

Select the H100 SXM5 for demanding AI tasks like training large language models exceeding 12 GB VRAM or high-throughput FP8 inference at 3958 TFLOPS. Its NVLink interconnect and SXM5 form factor support multi-GPU scaling in datacenters, ideal for enterprise research or production inference serving millions of tokens per hour.

Choose the RTX 3080 Ti for budget-conscious prototyping, fine-tuning small models under 10 GB, or gaming-integrated workflows at $0.08 per hour starting price. Its PCIe form factor and 320W TDP suit single-user desktops or small-scale cloud instances where 29.8 TFLOPS suffices without needing H100 SXM5's overhead.