RTX 3090 vs Tesla V100 32GB

The FP16 figure of 125 TFLOPS dense on the V100 variant exceeds the 35.6 TFLOPS figure on the RTX 3090 variant by a wide margin and this gap favors tensor core workloads that rely on dense FP16 arithmetic. The FP32 figure of 35.6 TFLOPS on the RTX 3090 variant exceeds the 15.7 TFLOPS figure on the V100 variant and this advantage appears in workloads that stay within single precision floating point. Both variants share memory bandwidth values near 900 GB/s so batch size scaling remains comparable when memory capacity limits are not reached. The 32 GB capacity on the V100 variant allows larger batches than the 24 GB capacity on the RTX 3090 variant in memory intensive training runs. The 350W TDP on the RTX 3090 variant exceeds the 300W TDP on the V100 variant and this difference affects power limited deployments. Direct comparison of dense FP16 figures shows the V100 variant ahead while FP32 figures show the RTX 3090 variant ahead.

Its PCIe form factor fits standard server slots without SXM2 requirements. Users select this variant for Stable Diffusion workloads that stay within 24 GB memory and value the higher FP32 throughput over dense FP16 capacity.

The V100 variant suits dense FP16 training tasks that require 125 TFLOPS dense FP16 performance and 32 GB HBM2 capacity. Its SXM2 option supports higher density racks in compatible systems. Users select this variant for scientific computing workloads that exploit the 125 TFLOPS dense FP16 rating and the larger memory pool for bigger datasets.