Quadro P6000 vs RTX 3060

Compute performance remains nearly identical between the two GPUs: the Quadro P6000 delivers 12.6 TFLOPS FP16 and FP32, while the RTX 3060 achieves 12.7 TFLOPS in both. This equivalence suggests comparable speeds for training and inference workloads that rely on half-precision or single-precision floating point operations. However, the Ampere architecture in the RTX 3060 introduces optimizations absent in Pascal, potentially yielding better real-world efficiency in modern frameworks.

Memory specifications create the primary divergence. The Quadro P6000's 24 GB GDDR5X VRAM doubles the RTX 3060's 12 GB GDDR6, enabling larger models or batch sizes without swapping to system RAM. Its 432 GB/s bandwidth exceeds the RTX 3060's 360 GB/s by 20 percent, reducing bottlenecks in memory-intensive tasks like LLM training where high batch sizes demand rapid data movement. Lower bandwidth on the RTX 3060 may limit scalability for such scenarios.

Power consumption impacts deployment: the RTX 3060's 170 W TDP versus 250 W allows denser cloud configurations and lower operational costs. For inference, where models fit within 12 GB, the newer architecture provides an edge in latency-sensitive applications.

The Quadro P6000 excels in scenarios requiring substantial VRAM, such as training large language models exceeding 12 GB. Its 24 GB capacity supports bigger batch sizes without performance degradation, and 432 GB/s bandwidth handles data transfers efficiently. Despite the $1.10 per hour pricing and 250 W TDP, it suits professional workflows prioritizing memory over cost.

Opt for the RTX 3060 in cost-sensitive or power-constrained environments, where its $0.03 per hour starting price and 170 W TDP deliver value. The 12 GB VRAM suffices for fine-tuning mid-sized models or inference on datasets fitting within that limit, while 12.7 TFLOPS performance matches the Quadro P6000 closely. Newer Ampere architecture enhances compatibility with recent software stacks.