GTX 1070 Ti vs Tesla P100

Peak compute shows minimal separation: the GTX 1070 Ti achieves 8.9 TFLOPS in both FP16 and FP32, closely trailing the P100's 9.3 TFLOPS per precision. This parity suggests similar throughput for compute-limited training or inference on smaller models. However, real-world ML tasks often hinge on memory: the P100's 732 GB/s bandwidth triples the 1070 Ti's 256 GB/s, enabling larger batch sizes and reducing data movement bottlenecks during gradient computations.

The P100's 16 GB HBM2 VRAM doubles the 1070 Ti's 8 GB GDDR5, accommodating bigger models or datasets without out-of-memory errors in fine-tuning or inference. For training, higher bandwidth accelerates memory-bound operations like convolutions; for inference, it supports higher concurrency. The 1070 Ti's lower 180 W TDP may appeal in power-sensitive setups, but the P100's NVLink enhances multi-GPU scaling for distributed workloads.

The GTX 1070 Ti excels in power-constrained or cost-sensitive environments where its 180 W TDP undercuts the P100's 250 W. It fits inference on models under 8 GB VRAM or gaming-related compute, delivering 8.9 TFLOPS FP32 without NVLink needs. No live cloud offers make it suitable for on-premises setups with modest batch sizes limited by 256 GB/s bandwidth.

Opt for the P100 in memory-heavy AI and HPC tasks: 16 GB HBM2 VRAM and 732 GB/s bandwidth handle large models and batches far better than the 1070 Ti's 8 GB and 256 GB/s. NVLink support aids multi-GPU training, and cloud pricing starts at $0.60 per hour. Its 9.3 TFLOPS FP32 edges out for sustained datacenter loads.