Quadro P4000 vs RTX 4080 SUPER

Compute performance differs dramatically between these GPUs: the Quadro P4000 delivers 5.3 TFLOPS in both FP16 and FP32, suitable for basic machine learning inference on small models. The RTX 4080 SUPER achieves 48.7 TFLOPS in FP16 and FP32, accelerating training and inference by nearly tenfold for deep learning tasks. This FP16 and FP32 parity in both cards supports mixed-precision workflows without bottlenecks, but the RTX 4080 SUPER's superior throughput handles larger neural networks efficiently. Memory bandwidth impacts real-world usage profoundly: the P4000's 243 GB/s limits batch sizes in training to small datasets, risking out-of-memory errors beyond 8 GB VRAM. The RTX 4080 SUPER's 717 GB/s bandwidth sustains high batch sizes with its 16 GB VRAM, ideal for processing extensive image or language model data. Power draw reflects efficiency gaps: 105W TDP for the P4000 versus 320W for the RTX 4080 SUPER, influencing cloud costs in prolonged runs.

The Quadro P4000 suits legacy professional applications requiring certified drivers, such as CAD software optimized for Pascal architecture. Its 105W TDP enables dense deployments in power-constrained environments, consuming less energy than the 320W RTX 4080 SUPER. At $0.51 per hour average pricing, it provides value for simple visualization tasks where 5.3 TFLOPS suffices and 8 GB VRAM handles modest datasets.

The RTX 4080 SUPER excels in contemporary AI workloads, leveraging 48.7 TFLOPS FP16 performance and 16 GB VRAM for LLM training or Stable Diffusion generation. Its 717 GB/s bandwidth supports large batch sizes unavailable on the P4000's 243 GB/s setup. Cloud pricing at $0.32 per hour average makes it economical for high-throughput compute despite the 320W TDP.