P100 vs RTX 5060

The RTX 5060's 23.1 TFLOPS in FP16 and FP32 significantly outpaces the P100's 9.3 TFLOPS, enabling roughly 2.5 times faster matrix operations critical for deep learning training and inference. This FP16/FP32 parity in both GPUs supports efficient mixed-precision workflows, but the Blackwell architecture's optimizations yield real-world speedups beyond raw TFLOPS, particularly in transformer models where attention mechanisms benefit from higher throughput.

Memory differences profoundly impact workloads: the P100's 16 GB HBM2 and 732 GB/s bandwidth handle larger batch sizes in memory-bound tasks like training large language models, reducing out-of-memory errors compared to the RTX 5060's 12 GB GDDR7 and 448 GB/s. Lower bandwidth on the RTX 5060 may constrain batch sizes in data-parallel training, potentially increasing iteration times by 60 percent in bandwidth-saturated scenarios. However, the RTX 5060's 180W TDP versus 250W allows denser deployments, cutting power costs by 28 percent per GPU.

Overall, compute-intensive inference favors the RTX 5060's superior FLOPS, while memory-heavy fine-tuning leans toward the P100's capacity.

Opt for the P100 in scenarios demanding high memory throughput, such as training models exceeding 12 GB VRAM or multi-GPU clusters via NVLink. Its 732 GB/s bandwidth excels in scientific simulations or large-batch LLM fine-tuning, where the RTX 5060's 448 GB/s would bottleneck performance. Despite higher average pricing at $0.25 per hour, the P100's 16 GB HBM2 justifies selection for legacy HPC workloads optimized for Pascal.

The RTX 5060 suits modern AI inference and gaming-accelerated tasks, leveraging 23.1 TFLOPS for 2.5 times the speed of the P100 at a lower average $0.15 per hour. Its 180W TDP enables cost-efficient scaling in cloud environments with abundant six live offers. Choose it for single-GPU deployments in Stable Diffusion or lightweight LLM serving, where Blackwell efficiencies outweigh the P100's memory edge.