MI355X vs TITAN Xp

Peak FP16 performance reaches 2300 TFLOPS on the MI355X, versus 12.1 TFLOPS on the TITAN Xp, enabling the former to train models roughly 190 times faster in half-precision tasks. FP32 matches at 2300 TFLOPS for MI355X and 12.1 TFLOPS for TITAN Xp, benefiting scientific simulations similarly. Equal FP16 and FP32 ratios on both suggest balanced tensor core utilization, but the MI355X's scale transforms training throughput for deep learning.

Memory bandwidth of 8000 GB/s on the MI355X supports massive batch sizes, such as processing datasets that exceed the TITAN Xp's 548 GB/s limit, reducing epochs needed for convergence. The 288 GB VRAM capacity handles models like large language models without splitting, unlike the 12 GB constraint forcing quantization or multi-GPU setups on TITAN Xp. For inference, MI355X FP8 at 4600 TFLOPS accelerates low-precision serving, unavailable on TITAN Xp.

Power draw contrasts sharply: 750W TDP for MI355X demands robust cooling, while 250W suits efficient legacy inference. Infinity Fabric interconnect on MI355X enables multi-GPU scaling, absent on TITAN Xp.

The MI355X excels in datacenter-scale AI training and inference requiring 288 GB HBM3e VRAM and 8000 GB/s bandwidth. Users handling large language models or scientific computing with datasets exceeding 12 GB benefit from its 2300 TFLOPS FP16/FP32 performance. OAM form factor and Infinity Fabric suit high-density server deployments.

The TITAN Xp fits legacy desktop setups or low-budget experiments with small models under 12 GB VRAM. Its 250W TDP and PCIe form factor enable easy integration into older workstations without high power infrastructure. Compatibility with Pascal-era software makes it viable for maintaining outdated pipelines at 12.1 TFLOPS FP16/FP32.