P100 vs RTX 3070

The RTX 3070 outperforms the P100 in raw compute with 20.3 TFLOPS for both FP16 and FP32, compared to the P100's 9.3 TFLOPS, translating to roughly twice the speed for training and inference in deep learning models using mixed precision. This FP16 and FP32 parity on both GPUs supports efficient half-precision training without accuracy loss, but the RTX 3070's Ampere architecture handles modern tensor cores better for transformer models. Memory bandwidth impacts batch sizes directly: the P100's 732 GB/s HBM2 sustains larger batches in memory-bound scenarios than the RTX 3070's 448 GB/s GDDR6, reducing data loading bottlenecks in tasks like large-scale simulations. For inference, the RTX 3070's higher TFLOPS enables faster latency on smaller models fitting within 8 GB VRAM, while the P100 excels with datasets exceeding 8 GB due to its 16 GB capacity. Power efficiency favors the RTX 3070 at 220W TDP, yielding better performance per watt for prolonged cloud runs.

The P100 suits workloads demanding high memory capacity and bandwidth, such as training models with datasets over 8 GB or scientific computing requiring 16 GB HBM2. Its 732 GB/s bandwidth supports large batch sizes without slowdowns, and NVLink interconnect enables multi-GPU scaling unavailable on the RTX 3070. Cloud users prioritizing PCIe or SXM2 form factors for enterprise setups find the P100 ideal despite higher average pricing of $0.25 per hour.

The RTX 3070 excels in compute-heavy tasks like fine-tuning or inference where 20.3 TFLOPS FP16 performance doubles the P100's 9.3 TFLOPS, offering superior speed at lower costs from $0.04 per hour. With more live offers across six providers and a 220W TDP, it provides better value for consumer-grade PCIe deployments in gaming-accelerated ML. Users benefit from Ampere's modern features for tasks fitting within 8 GB VRAM.