Tesla P100 vs RTX 4070 SUPER

The RTX 4070 SUPER outperforms the P100 in compute throughput: 35.5 TFLOPS versus 9.3 TFLOPS in FP16 and FP32 represents a 3.8 times increase. This disparity accelerates deep learning training and inference, where half-precision FP16 dominates modern workflows, reducing epochs from days to hours on equivalent datasets.

Memory profiles differ markedly. The P100's 732 GB/s bandwidth exceeds the RTX 4070 SUPER's 504 GB/s by 45 percent, supporting larger batch sizes in training loops and minimizing data transfer bottlenecks. Coupled with 16 GB versus 12 GB VRAM, the P100 handles bigger models or higher resolutions without out-of-memory errors.

Efficiency metrics favor the RTX 4070 SUPER. Its 220 W TDP yields 0.161 TFLOPS per watt, compared to the P100's 0.037 TFLOPS per watt at 250 W. Ada Lovelace enhancements further boost real-world ML throughput beyond raw specs.

The P100 suits memory-bound workloads like large-scale scientific simulations. Its 16 GB HBM2 VRAM and 732 GB/s bandwidth enable batch sizes 45 percent larger than the RTX 4070 SUPER's 12 GB GDDR6X at 504 GB/s. NVLink interconnect facilitates multi-GPU scaling, unavailable on the RTX 4070 SUPER. At $0.07 per hour starting price, it offers unmatched value for budget deployments.

Select the RTX 4070 SUPER for compute-heavy AI tasks such as model training. The 35.5 TFLOPS rating crushes the P100's 9.3 TFLOPS, slashing inference latency by up to 3.8 times. Lower 220 W TDP delivers superior efficiency at 0.161 TFLOPS per watt. Ada architecture optimizes modern frameworks, ideal for single-node prosumer setups.