Quadro P4000 vs V100

The V100 outperforms the Quadro P4000 dramatically in compute capabilities. Its FP16 performance of 125 TFLOPS dwarfs the P4000's 5.3 TFLOPS, accelerating mixed-precision training in deep learning by enabling faster convergence on large models. The FP32 rating of 15.7 TFLOPS on the V100 versus 5.3 TFLOPS on the P4000 benefits single-precision tasks like scientific simulations, reducing runtime significantly.

Memory specifications further highlight the gap: the V100's 900 GB/s bandwidth and 16-32 GB HBM2 allow larger batch sizes in training, minimizing overhead from data transfers compared to the P4000's 243 GB/s and 8 GB GDDR5. This enables handling datasets that exceed the P4000's capacity, preventing out-of-memory errors in inference pipelines.

Power efficiency differs as well. The P4000's 105W TDP suits edge deployments, but the V100's 300W supports sustained high throughput via NVLink, ideal for clustered environments where interconnect speed matters.

The Quadro P4000 suits scenarios demanding low power and modest performance. With a 105W TDP and pricing from $0.51 per hour across 6 offers, it fits visualization tasks or light CAD workloads where 5.3 TFLOPS FP32 suffices and 8 GB VRAM handles smaller models. Its PCIe form factor simplifies integration in single-GPU cloud instances without multi-node complexity.

The Tesla V100 excels in demanding AI and HPC applications. Offering 125 TFLOPS FP16 and 15.7 TFLOPS FP32, it accelerates LLM training and inference, while 16-32 GB HBM2 and 900 GB/s bandwidth support large batches. Available from $0.10 per hour across 72 offers, NVLink enables efficient scaling in multi-GPU setups.