P100 vs Quadro RTX 4000

The P100 delivers superior compute throughput at 9.3 TFLOPS FP32 compared to 7.1 TFLOPS on the Quadro RTX 4000, a 31 percent increase that accelerates deep learning training and FP32 inference. Both GPUs maintain equal FP16 and FP32 rates, but P100's edge supports faster iterations in model optimization. In real-world scenarios, this means P100 completes compute-bound tasks quicker, such as matrix multiplications in neural networks. Memory differences are stark: P100's 16 GB HBM2 versus 8 GB GDDR6 allows double the model size or batch sizes, preventing out-of-memory errors in training large datasets. Bandwidth of 732 GB/s on P100 versus 416 GB/s enables higher data transfer rates, reducing bottlenecks during large-batch inference and improving throughput by up to 76 percent in memory-intensive operations. Turing's Tensor cores on Quadro RTX 4000 offer specialized INT8/FP16 acceleration absent in Pascal, benefiting certain inference pipelines.

Select the P100 for memory-heavy workloads like training models exceeding 8 GB: its 16 GB HBM2 capacity handles larger batches without splitting. High bandwidth of 732 GB/s supports rapid data movement, ideal for scientific simulations or deep learning with extensive datasets. Pricing at an average $0.25 per hour provides cost efficiency over the Quadro RTX 4000's $0.56 per hour, especially for prolonged cloud runs.

Opt for the Quadro RTX 4000 in power-limited setups: its 160W TDP consumes 36 percent less energy than P100's 250W, suiting dense cloud instances. The Turing architecture includes Tensor cores for optimized mixed-precision inference, outperforming Pascal in INT8 tasks despite lower 7.1 TFLOPS FP32. Greater availability across five cloud offers at $0.56 per hour fits short visualization or prototyping jobs.