RTX 3060 vs Tesla V100 16GB

The V100 dominates FP16 performance at 125 TFLOPS compared to the RTX 3060's 12.7 TFLOPS: this disparity accelerates deep learning training where tensor cores handle matrix operations in half-precision, enabling up to 10 times faster iterations on large neural networks. FP32 rates remain close at 15.7 TFLOPS for V100 versus 12.7 TFLOPS for RTX 3060, meaning single-precision tasks like scientific simulations show modest gains on the datacenter GPU.

Memory bandwidth reveals another divide: V100's 900 GB/s versus 360 GB/s on RTX 3060 supports substantially larger batch sizes in training and inference, minimizing data transfer bottlenecks and improving throughput for models exceeding 12 GB VRAM. The V100's 16 GB HBM2 holds an edge over 12 GB GDDR6, accommodating bigger datasets without swapping to host memory.

Power and interconnects influence real-world deployment: RTX 3060's 170W TDP allows denser cloud instances than V100's 300W, but NVLink on V100 enables faster multi-GPU scaling for distributed training versus RTX 3060's standard PCIe.

The RTX 3060 stands out for cost-sensitive inference and prototyping: cloud pricing begins at $0.03 per hour with an average of $0.07 per hour across 11 offers, far below V100's $0.82 per hour average. Its 170W TDP and PCIe form factor suit low-power, single-GPU cloud instances where 12.7 TFLOPS FP32 performance and 12 GB VRAM suffice for models under 10 billion parameters.

Ampere architecture optimizations make RTX 3060 ideal for generative tasks like Stable Diffusion, balancing efficiency without V100's overhead.

Opt for V100 in high-throughput training scenarios: 125 TFLOPS FP16 delivers dramatically faster mixed-precision compute than RTX 3060's 12.7 TFLOPS, cutting epochs on large language models. The 900 GB/s bandwidth and 16 GB HBM2 enable massive batch sizes, enhancing stability and speed.

NVLink interconnect and SXM2 form factor excel in multi-GPU clusters, where PCIe-limited RTX 3060 falls short despite lower 170W TDP.