RTX 3060 vs Tesla V100 32GB

FP16 performance defines a stark divide: the V100 achieves 125 TFLOPS versus the RTX 3060's 12.7 TFLOPS, enabling up to 10 times faster mixed-precision training and inference for large neural networks. This delta stems from Volta's advanced Tensor Cores optimized for deep learning, making V100 ideal for workloads like transformer training where half-precision dominates. FP32 rates remain close at 15.7 TFLOPS for V100 and 12.7 TFLOPS for RTX 3060, so single-precision tasks show modest V100 edges of about 24 percent.

Memory specs further favor V100 for scale: 900 GB/s bandwidth and 32 GB HBM2 versus 360 GB/s and 12 GB GDDR6 allow larger batch sizes and models without swapping, reducing training times by supporting datasets up to 2.7 times larger in memory capacity. RTX 3060 suits smaller models or inference with its lower 170W TDP versus 300W, yielding better power efficiency at 75 watts per TFLOPS FP32 compared to V100's 19 watts per TFLOPS. In real-world terms, V100 accelerates FP16-heavy pipelines like LLM pretraining, while RTX 3060 handles cost-sensitive batch inference effectively.

The RTX 3060 excels in budget-driven scenarios such as Stable Diffusion image generation or lightweight LLM inference, where its 12.7 TFLOPS FP16 suffices for real-time serving at $0.07 per hour average cost. Lower 170W TDP and PCIe simplicity make it preferable for small-scale cloud deployments or prototyping without NVLink needs.

Fine-tuning compact models under 12 GB also favors RTX 3060, as its Ampere efficiency avoids V100's 14 times higher hourly pricing for marginal FP32 gains.

Opt for V100 32GB in high-throughput training of large language models, leveraging 125 TFLOPS FP16 and 900 GB/s bandwidth to process batches twice as large as RTX 3060's capacity. Scientific computing benefits from 32 GB HBM2 and NVLink for multi-GPU simulations requiring 15.7 TFLOPS FP32.

Legacy datacenter workflows with SXM2 form factor demand V100's proven 300W endurance despite $1.01 per hour costs, outperforming in memory-bound tasks by factors tied to its 2.5 times bandwidth advantage.