H100 SXM5 vs TITAN Xp

H100 SXM5 outperforms TITAN Xp dramatically in machine learning workloads due to its FP16 rating of 1979 TFLOPS versus 12.1 TFLOPS: this enables 163 times faster half-precision training and inference. The FP32 performance of 67 TFLOPS on H100 SXM5 exceeds TITAN Xp's 12.1 TFLOPS by over fivefold, benefiting general compute tasks. FP8 capability at 3958 TFLOPS on H100 SXM5 accelerates modern inference further, absent in TITAN Xp. Memory bandwidth of 3350 GB/s on H100 SXM5 supports massive batch sizes in training large models, while 548 GB/s on TITAN Xp limits them severely for datasets exceeding 12 GB VRAM. In real-world terms, H100 SXM5 handles models like 70B parameter LLMs that TITAN Xp cannot load. Power draw reflects this: 700W TDP for H100 SXM5 versus 250W for TITAN Xp demands robust cooling and infrastructure.

Choose NVIDIA H100 SXM5 for large-scale AI training and inference requiring over 80 GB VRAM, such as LLMs with billions of parameters. Its 1979 TFLOPS FP16 and 3350 GB/s bandwidth enable efficient handling of huge batches in cloud environments, available from $0.80/hr. Datacenter setups benefit from NVLink and InfiniBand for multi-GPU scaling, unavailable on TITAN Xp.

Select NVIDIA TITAN Xp for budget-conscious legacy applications or small-scale tasks fitting within 12 GB VRAM, like basic prototyping. Its 250W TDP suits standard desktops without high power infrastructure, and no cloud offers make it ideal for on-premises consumer hardware. FP16 and FP32 both at 12.1 TFLOPS suffice for non-demanding inference on modest models.