Tesla P100 vs RTX 4070 Ti

The RTX 4070 Ti provides 29.1 TFLOPS in FP16 and FP32, exactly three times the P100's 9.3 TFLOPS per precision. This superiority accelerates training cycles and inference throughput in compute-intensive deep learning pipelines. Equal FP16 and FP32 rates on both GPUs ensure no precision downgrade penalty, optimizing frameworks like TensorFlow or PyTorch for mixed-precision workflows. Higher throughput on the RTX 4070 Ti shortens epochs in large model training. The P100's 732 GB/s bandwidth outpaces the RTX 4070 Ti's 504 GB/s: this enables larger batch sizes in memory-constrained scenarios, reducing overhead in data loading for inference or training. Lower bandwidth on the RTX 4070 Ti may bottleneck massive datasets despite superior flops.

The P100 suits memory-bound applications: its 16 GB HBM2 exceeds the RTX 4070 Ti's 12 GB GDDR6X, accommodating larger models or datasets. Superior 732 GB/s bandwidth supports expansive batch sizes in training simulations. NVLink interconnect enables efficient multi-GPU scaling in datacenter clusters. Starting at $0.07 per hour, it delivers value for high-memory scientific tasks.

The RTX 4070 Ti dominates compute-focused workloads with 29.1 TFLOPS versus 9.3 TFLOPS, tripling performance for training and inference. Its 200W TDP undercuts the P100's 250W for better efficiency in power-limited clouds. Ada Lovelace architecture enhances features like ray tracing for hybrid AI graphics tasks. Average $0.22 per hour pricing across 5 offers ensures greater availability.