MI325X vs V100

MI325X outperforms V100 dramatically in raw compute: 1307 TFLOPS FP16 on MI325X towers over V100's 125 TFLOPS, enabling faster training of large language models where mixed precision dominates. The equal 1307 TFLOPS FP32 on MI325X contrasts with V100's mere 15.7 TFLOPS, benefiting simulations requiring full precision. For inference, MI325X's 2614 TFLOPS FP8 capability accelerates low-precision deployments unavailable on V100. Memory differences prove critical: 256 GB VRAM on MI325X supports massive batch sizes for models exceeding 32 GB, while V100 limits scale. Bandwidth of 6000 GB/s on MI325X reduces data bottlenecks versus 900 GB/s on V100, improving throughput in memory-bound tasks like fine-tuning. Power draw of 750W TDP on MI325X demands robust cooling, unlike V100's efficient 300W.

MI325X suits large-scale AI training and inference with models demanding over 32 GB VRAM, such as trillion-parameter LLMs. Its 256 GB HBM3e and 6000 GB/s bandwidth handle enormous batch sizes without swapping, while 1307 TFLOPS FP16/FP32 accelerates iterations. Deploy it in data centers via OAM form factor and Infinity Fabric for multi-GPU scaling.

V100 fits budget-conscious or legacy workflows, available from $0.10 per hour averaging $0.94 across 72 offers. Its 300W TDP enables low-power clusters, and NVLink or PCIe 3.0 supports established NVIDIA software stacks. Choose it for scientific computing or fine-tuning under 32 GB where cost trumps peak performance.