Quadro P6000 vs RTX 3090

The architectural shift from Pascal to Ampere yields substantial gains for the RTX 3090. Its 35.6 TFLOPS in FP32 surpasses the Quadro P6000's 12.6 TFLOPS by 2.8 times, accelerating compute-bound tasks like model training. FP16 performance follows the same ratio at 35.6 TFLOPS versus 12.6 TFLOPS, enhancing mixed-precision training and inference where tensor cores in Ampere provide optimized throughput.

Memory bandwidth represents a critical delta: the RTX 3090's 936 GB/s enables larger batch sizes in memory-bound scenarios compared to the P6000's 432 GB/s. For instance, during LLM training, higher bandwidth reduces data transfer bottlenecks, allowing effective utilization of the full 24 GB VRAM without stalling. Inference workloads benefit similarly, supporting higher concurrency on the newer card.

Power consumption differs at 350 W for the RTX 3090 versus 250 W for the P6000, yet the performance per watt favors Ampere by approximately 1.4 times in FP32. Interconnect options include NVLink on the RTX 3090 for multi-GPU scaling, absent on the P6000, which impacts distributed training efficiency.

The Quadro P6000 suits legacy professional applications requiring certified Pascal drivers, such as specific CAD or visualization software optimized for its 24 GB GDDR5X VRAM. Its lower 250 W TDP fits power-constrained cloud instances where 350 W exceeds limits. At $1.10 per hour average, it provides reliability for workflows incompatible with consumer-grade Ampere cards.

The RTX 3090 excels in modern AI and machine learning tasks leveraging its Ampere architecture, delivering 35.6 TFLOPS FP32 and 936 GB/s bandwidth for faster training and inference. NVLink support enables efficient multi-GPU setups, and cloud pricing at $0.42 per hour average across 49 offers makes it far more economical than the P6000's $1.10 per hour. Choose it for high-throughput workloads demanding generational performance leaps.