RTX 3080 vs V100

Volta's V100 excels in FP16 at 125 TFLOPS due to advanced tensor cores, enabling faster mixed-precision training for deep learning models compared to the RTX 3080's 29.8 TFLOPS FP16. This delta accelerates gradient computations in training pipelines, often yielding 4x speedups over FP32-only workflows on the V100. However, the RTX 3080 matches its FP16 with 29.8 TFLOPS FP32, suiting single-precision inference or simulations where V100 lags at 15.7 TFLOPS FP32. Memory bandwidth favors the V100's 900 GB/s HBM2 over 760 GB/s GDDR6X, supporting larger batch sizes in memory-bound tasks like transformer training without swapping to host RAM. The V100's 16 to 32 GB VRAM capacity handles massive datasets, while 10 to 12 GB on RTX 3080 limits scale for very large models. Power draw remains close at 300 W versus 320 W, but NVLink interconnect on V100 enables efficient multi-GPU scaling absent on the PCIe-only RTX 3080.

The RTX 3080 suits cost-sensitive deployments starting at $0.06 per hour. Its balanced 29.8 TFLOPS FP16 and FP32 performance excels in inference-heavy workloads or fine-tuning smaller models within 10 to 12 GB VRAM limits. Users prioritizing affordability over peak datacenter features benefit from its newer Ampere architecture at an average $0.15 per hour.

Opt for the V100 when high VRAM of 16 to 32 GB and 900 GB/s bandwidth are essential for large-batch training. Its 125 TFLOPS FP16 dominates mixed-precision deep learning, and NVLink supports multi-GPU clusters despite higher average pricing of $0.94 per hour. Datacenter environments leverage its SXM2 form factor for sustained high-throughput compute.