Quadro P4000 vs Quadro RTX 4000

The Quadro RTX 4000 outperforms the P4000 by 34 percent in FP32 compute: 7.1 TFLOPS compared to 5.3 TFLOPS, directly translating to faster matrix operations in machine learning training and inference. This delta accelerates neural network processing, reducing epoch times in training by handling more floating-point operations per second. For FP16 workloads, the same 7.1 TFLOPS versus 5.3 TFLOPS ratio benefits half-precision models common in modern AI.

Memory bandwidth presents the largest gap: 416 GB/s on the RTX 4000 versus 243 GB/s on the P4000, enabling larger batch sizes in training without memory bottlenecks. Higher bandwidth supports data-intensive tasks like large language model inference, where frequent memory access dominates. GDDR6 memory on the RTX 4000 further improves efficiency over the P4000's GDDR5.

Power draw differs significantly at 160W TDP for the RTX 4000 against 105W for the P4000, potentially limiting multi-GPU scaling in constrained environments but allowing the RTX 4000 to sustain peak performance longer in compute-heavy scenarios.

The Quadro P4000 suits budget-conscious users prioritizing cost and efficiency: cloud pricing starts at $0.51 per hour across 6 offers, undercutting the RTX 4000's $0.56 per hour. Its 105W TDP enables deployment in power-limited servers or laptops without thermal throttling.

Choose the P4000 for legacy Pascal-optimized software or lighter visualization tasks where 5.3 TFLOPS and 243 GB/s bandwidth suffice, avoiding the 160W demands of newer GPUs.

The Quadro RTX 4000 excels in performance-critical applications: 7.1 TFLOPS FP32 compute surpasses the P4000's 5.3 TFLOPS, and 416 GB/s bandwidth handles larger datasets effectively.

Opt for the RTX 4000 in Turing-compatible workflows like advanced rendering or AI inference, where the modest $0.05 hourly premium justifies 34 percent faster processing and GDDR6 advantages.