Tesla P100 vs RTX 5060 Ti

Compute throughput marks the clearest divide: the RTX 5060 Ti achieves 23.1 TFLOPS in FP16 and FP32, over twice the P100's 9.3 TFLOPS, translating to faster model training and inference runs by up to 2.5 times in deep learning tasks. Equal FP16 and FP32 rates on both ensure balanced performance across precision modes, but the RTX 5060 Ti's edge accelerates gradient computations and forward passes. Memory bandwidth favors the P100 at 732 GB/s over 448 GB/s: this supports larger batch sizes in training, reducing overhead from frequent data transfers. The P100's 16 GB HBM2 VRAM exceeds the RTX 5060 Ti's 12 GB GDDR7, enabling deployment of bigger models without quantization. Power draw reveals efficiency gains: the RTX 5060 Ti's 180W TDP versus 250W cuts energy costs in prolonged sessions. Newer Blackwell architecture likely enhances tensor core utilization for AI-specific operations beyond raw specs.

The P100 excels in memory-intensive scientific computing: its 732 GB/s bandwidth and 16 GB HBM2 handle large datasets and simulations with bigger batches than the RTX 5060 Ti's 448 GB/s and 12 GB. NVLink interconnect enables multi-GPU scaling unavailable on the PCIe-only RTX 5060 Ti, ideal for distributed legacy HPC workloads. At $0.07 per hour availability, it suits low-budget, high-memory needs without refactoring code.

The RTX 5060 Ti dominates modern AI pipelines: 23.1 TFLOPS doubles P100 performance for training and inference, halving compute times at lower 180W TDP and $0.15 per hour average cost. Greater availability across 10 cloud offers ensures scalability. Blackwell features optimize LLM fine-tuning and diffusion models beyond Pascal capabilities.