RTX 3080 vs Tesla V100 16GB

FP16 performance disparity favors the V100 at 125 TFLOPS over RTX 3080's 29.8 TFLOPS, accelerating half-precision training common in deep learning where models like transformers benefit from reduced precision without accuracy loss. This enables V100 to process larger models faster in FP16-dominant workflows. Conversely, RTX 3080's 29.8 TFLOPS FP32 surpasses V100's 15.7 TFLOPS, suiting single-precision tasks in scientific simulations or older codebases requiring full FP32.

Memory specifications impact real-world throughput: V100's 16 GB HBM2 and 900 GB/s bandwidth handle bigger batch sizes in memory-bound training, reducing overhead in large language model optimization compared to RTX 3080's 10 to 12 GB GDDR6X at 760 GB/s. Higher bandwidth on V100 minimizes data starvation during intensive matrix operations.

Power and interconnects show parity with TDPs at 320W for RTX 3080 and 300W for V100, but V100's NVLink enables scalable multi-GPU setups for distributed training, outperforming RTX 3080's PCIe-only connectivity in cluster environments.

RTX 3080 suits cost-sensitive projects leveraging its pricing from $0.06 per hour averaging $0.17 per hour. Balanced FP32 at 29.8 TFLOPS excels in inference or fine-tuning where single-precision dominates, and Ampere architecture supports modern features like improved ray tracing for graphics-ML hybrids.

Smaller-scale deployments benefit from 10 to 12 GB VRAM and 760 GB/s bandwidth when batch sizes fit within limits, avoiding V100's higher average $0.82 per hour cost.

V100 excels in FP16-heavy training with 125 TFLOPS, ideal for large-scale deep learning where its 16 GB HBM2 and 900 GB/s bandwidth support massive batches. NVLink interconnect facilitates multi-GPU scaling unavailable on RTX 3080.

Legacy HPC or datacenter workflows prioritize V100's PCIe 3.0 and SXM2 form factors despite pricing from $0.10 per hour averaging $0.82 per hour, offering superior memory for memory-intensive simulations.