RTX 5060 vs V100

The V100 achieves 125 TFLOPS in dense FP16 compared to its 15.7 TFLOPS in FP32 which supports larger batch sizes during training and inference tasks. The RTX 5060 lists no dense FP16 figure and instead provides 19.2 TFLOPS in FP32 along with 614 TFLOPS in FP4 with sparsity. Memory bandwidth of 900 GB/s on the V100 exceeds the 448 GB/s on the RTX 5060 and therefore permits greater data throughput for memory intensive operations. The 300W TDP of the V100 versus the 145W TDP of the RTX 5060 affects sustained performance in dense FP16 workloads. Ratios between the given FP32 figures show the RTX 5060 at 19.2 TFLOPS against the V100 at 15.7 TFLOPS while sparsity labeled figures cannot be compared directly to dense figures.

Choose the RTX 5060 when the whole job fits in 8 to 16 GB of GDDR7 on one card: Stable Diffusion, small vision models, or a small LLM quantized to FP4 or FP8, formats the V100 cannot run. Its 19.2 TFLOPS of FP32 beats the V100 at 15.7 TFLOPS, but 448 GB/s against 900 GB/s means decode runs at about half the speed once the model fits on both. It is a PCIe 5.0 consumer card with no NVLink and no published dense FP16 tensor rating, so it is not a multi-GPU training part.

The V100 fits workloads that require 125 TFLOPS in dense FP16 and up to 32 GB HBM2 memory. Its 900 GB/s bandwidth supports larger batch sizes in training compared to the 448 GB/s alternative. The 300W TDP accommodates data center setups that need NVLink interconnect.