MI355X vs P100

The MI355X dominates in raw compute with 2300 TFLOPS in FP16 and FP32, a factor of approximately 247 times higher than the P100's 9.3 TFLOPS in those precisions. This delta translates to dramatically faster model training and inference for deep learning tasks, where FP16 accelerates matrix operations without significant precision loss. For inference, the MI355X's FP8 capability at 4600 TFLOPS further enhances throughput for quantized models.

Memory bandwidth profoundly impacts real-world performance: the MI355X's 8000 GB/s supports massive batch sizes in training large language models, reducing iteration times compared to the P100's 732 GB/s limitation. The P100 struggles with datasets exceeding 16 GB VRAM, causing out-of-memory errors, whereas the MI355X handles models up to 288 GB seamlessly. In training scenarios, higher bandwidth minimizes data loading bottlenecks, enabling efficient scaling across multi-GPU setups via Infinity Fabric versus NVLink.

Power efficiency reveals trade-offs: the P100's 250 W TDP suits low-density clusters, but its compute shortfall limits utility in FP16-heavy workflows like fine-tuning.

The MI355X excels in demanding AI applications requiring vast memory, such as training large language models with billions of parameters. Its 288 GB HBM3e VRAM and 8000 GB/s bandwidth accommodate enormous batch sizes, while 2300 TFLOPS in FP16 and FP32 ensure rapid convergence. Data centers prioritizing cutting-edge performance over immediate availability select this GPU for CDNA 4's advancements.

The P100 fits budget-limited environments or legacy software incompatible with newer architectures. At $0.07 per hour average $0.25 per hour, it provides accessible compute for small-scale inference or scientific simulations under 16 GB VRAM. Low 250 W TDP enables dense deployments in power-constrained setups without needing Infinity Fabric scaling.