RTX 4060 vs Tesla V100 32GB

The V100's FP16 performance of 125 TFLOPS dwarfs the RTX 4060's 15.1 TFLOPS, enabling faster training and inference for deep learning models optimized for half-precision arithmetic, which constitutes over 90 percent of modern AI workloads. FP32 rates are comparable at 15.7 TFLOPS for the V100 and 15.1 TFLOPS for the RTX 4060, meaning single-precision tasks like certain scientific simulations show minimal differences. This FP16 delta positions the V100 for accelerating gradient computations in training by up to 8 times in mixed-precision setups.

Memory bandwidth defines batch size capabilities: the V100's 900 GB/s supports larger batches in memory-bound operations, such as transformer model inference, compared to the RTX 4060's 272 GB/s, which limits it to smaller datasets. The V100's 32 GB HBM2 versus 8 GB GDDR6 allows loading models exceeding 8 GB without swapping, reducing latency in large language model deployments. Higher TDP of 300W on the V100 sustains peak throughput longer than the 115W RTX 4060 in prolonged runs.

The RTX 4060 suits lightweight inference and fine-tuning of small models under 8 GB, leveraging its 2023 Ada Lovelace architecture for efficiency at 115W TDP. Developers on budgets prioritize it for Stable Diffusion or gaming-integrated AI where FP32 at 15.1 TFLOPS matches most needs without datacenter overhead. Its PCIe form factor fits consumer setups lacking NVLink.

The V100 excels in high-FP16 workloads like LLM training, delivering 125 TFLOPS to process large batches via 900 GB/s bandwidth and 32 GB VRAM. Cloud users benefit from its $0.29 per hour starting price for scalable scientific computing or multi-GPU clusters with NVLink. Legacy compatibility justifies it over newer options in established pipelines.