RTX 2080 vs Tesla V100 16GB

V100's FP16 performance reaches 125 TFLOPS: this enables rapid mixed-precision training, far surpassing RTX 2080's 10.1 TFLOPS and accelerating deep learning iterations. FP32 compute at 15.7 TFLOPS on V100 offers a 55 percent advantage over RTX 2080's 10.1 TFLOPS, benefiting inference and simulations reliant on single precision. Memory bandwidth of 900 GB/s on V100 supports larger batch sizes during training, minimizing data transfer bottlenecks unlike RTX 2080's 616 GB/s. The 16 GB HBM2 on V100 handles extensive model parameters without out-of-memory errors, while RTX 2080's 8-11 GB GDDR6 limits scale for large datasets. In real-world training, V100 reduces epochs through tensor core efficiency implied by FP16 dominance. For inference, V100 sustains higher throughput on memory-bound tasks due to superior bandwidth and capacity. Power draw at 300W for V100 versus 215W for RTX 2080 influences cluster density, though V100's PCIe and SXM2 versatility aids multi-GPU setups via NVLink.

RTX 2080 fits budget-conscious inference or creative workloads like Stable Diffusion: its pricing from $0.05 per hour and 215W TDP keep operational costs low. With 8-11 GB VRAM and 10.1 TFLOPS FP32, it manages models under 8 GB effectively without excessive spend. Users prioritizing affordability over peak performance select it for prototyping or small-scale deployments.

V100 excels in intensive training and fine-tuning: 125 TFLOPS FP16 and 16 GB HBM2 enable handling of large language models efficiently. Bandwidth at 900 GB/s supports high-batch training, outperforming RTX 2080 in time-to-result. Despite higher average pricing of $0.82 per hour, its datacenter optimizations justify selection for production AI pipelines.