GTX 1080 vs Tesla V100 32GB

The V100 demonstrates superior compute capability, particularly in FP16 at 125 TFLOPS versus the GTX 1080's 8.9 TFLOPS, enabling faster deep learning training and inference with half-precision formats common in modern frameworks. In FP32, the V100 achieves 15.7 TFLOPS against 8.9 TFLOPS, benefiting single-precision tasks like scientific simulations. This FP16 to FP32 delta on the V100 supports mixed-precision training, reducing memory usage while accelerating convergence compared to the GTX 1080's balanced but lower 8.9 TFLOPS across both.

Memory bandwidth profoundly impacts workloads: the V100's 900 GB/s allows larger batch sizes in training, processing more data per iteration without bottlenecks, unlike the GTX 1080's 320 GB/s which limits scalability for memory-intensive models. Higher VRAM on the V100, up to 32 GB HBM2, accommodates larger models or datasets versus the GTX 1080's 8 to 11 GB GDDR5X, reducing swapping and improving throughput in inference scenarios.

Power efficiency follows suit: the V100's 300W TDP sustains higher performance density, while the GTX 1080's 180W suits lighter loads but throttles under sustained AI demands.

The GTX 1080 suits budget-conscious users for lightweight inference or gaming-related tasks in the cloud. Its 8 to 11 GB VRAM and 320 GB/s bandwidth handle small models effectively at $0.30 per hour average, with lower 180W TDP minimizing costs in low-density deployments. Scenarios include prototyping simple neural networks or Stable Diffusion at reduced scales where 8.9 TFLOPS FP32 suffices without needing datacenter features.

Opt for the V100 32GB in demanding AI training or large-scale inference, leveraging 125 TFLOPS FP16 and 15.7 TFLOPS FP32 for rapid iterations. The 900 GB/s bandwidth and up to 32 GB HBM2 enable massive batch sizes and complex models, ideal despite 300W TDP when NVLink interconnects boost multi-GPU scaling. At $0.29 per hour entry pricing across 46 offers, it excels in production ML pipelines.