Quadro P6000 vs RTX 3080

The RTX 3080 demonstrates superior raw performance with 29.8 TFLOPS in both FP16 and FP32, doubling the Quadro P6000's 12.6 TFLOPS rates. This advantage accelerates deep learning training and inference by approximately 2.4 times in FP32-dominant workflows, such as convolutional neural networks. Higher FP16 throughput on the RTX 3080 benefits mixed-precision training common in large language models, reducing overall iteration times. Memory bandwidth plays a critical role: 760 GB/s on the RTX 3080 supports larger batch sizes than the P6000's 432 GB/s, minimizing data transfer bottlenecks during gradient computations. The Quadro P6000 counters with 24 GB VRAM versus 10 to 12 GB on the RTX 3080, enabling single-GPU handling of models exceeding 12 GB without model parallelism, vital for memory-bound inference on oversized transformers. Power draw differs at 320W TDP for RTX 3080 versus 250W for P6000, influencing multi-GPU scaling in dense clusters.

Select the Quadro P6000 for workloads demanding over 12 GB VRAM, such as single-GPU training or inference of large language models with 24 GB fitting parameters up to 70 billion at FP16. Its 24 GB GDDR5X capacity avoids sharding techniques required on the RTX 3080's 10 to 12 GB, simplifying deployment in memory-constrained environments. Legacy professional software optimized for Quadro cards may leverage its Pascal-specific drivers despite higher $1.10 per hour pricing.

Opt for the RTX 3080 in performance-sensitive tasks where 29.8 TFLOPS FP32 outperforms the P6000's 12.6 TFLOPS, ideal for rapid prototyping or high-throughput inference. Its 760 GB/s bandwidth handles larger batches efficiently, and Ampere architecture supports advanced features like Tensor Cores for modern AI frameworks. At $0.13 per hour average, it offers superior value for budget-conscious users across 8 cloud providers.