Quadro P5000 vs RTX 4080 SUPER

Compute capabilities define the primary disparity: the RTX 4080 SUPER achieves 48.7 TFLOPS in FP16 and FP32, surpassing the Quadro P5000's 8.9 TFLOPS by a factor of 5.5. This translates to significantly faster model training times, where a workload requiring 10 hours on the P5000 completes in under 2 hours on the 4080 SUPER, and similar gains in inference latency for real-time applications.

Memory bandwidth impacts data handling efficiency: 717 GB/s on the RTX 4080 SUPER versus 288 GB/s on the Quadro P5000 enables larger batch sizes in training and fine-tuning, reducing overhead and boosting overall throughput by minimizing memory stalls. Higher bandwidth proves crucial for LLMs where datasets exceed 16 GB VRAM limits through frequent transfers.

Power consumption differs notably, with the RTX 4080 SUPER at 320W TDP compared to 180W for the P5000, influencing suitability for dense cloud deployments but offset by the newer GPU's superior efficiency per watt (0.152 TFLOPS/W versus 0.049 TFLOPS/W).

The Quadro P5000 fits legacy professional workflows optimized for Pascal architecture, such as CAD simulations certified under its Quadro drivers. Its 180W TDP suits power-limited cloud instances where the RTX 4080 SUPER's 320W exceeds capacity, avoiding deployment failures. At $0.78 per hour across six providers, it remains viable when newer GPUs face availability shortages.

The RTX 4080 SUPER dominates modern machine learning tasks with 48.7 TFLOPS FP16/FP32 performance and 717 GB/s bandwidth, enabling efficient LLM training and inference at $0.32 average hourly cost. Its Ada Lovelace features like improved tensor cores accelerate Stable Diffusion generation by over 5 times compared to the P5000's 8.9 TFLOPS. Availability across three providers starting at $0.17 per hour makes it ideal for high-throughput cloud scaling.