GTX 1080 vs V100

The V100's FP16 performance of 125 TFLOPS dwarfs the GTX 1080's 8.9 TFLOPS: this enables dramatically faster mixed-precision training for deep learning models, reducing epochs from days to hours on large datasets. FP32 throughput at 15.7 TFLOPS on V100 versus 8.9 TFLOPS on GTX 1080 supports superior single-precision inference and simulations, minimizing accuracy loss in compute-intensive pipelines.

Memory bandwidth defines workload feasibility: V100's 900 GB/s allows batch sizes up to 4 times larger than GTX 1080's 320 GB/s limit, alleviating bottlenecks in transformer models or CNNs with high-resolution inputs. VRAM capacity of 16 to 32 GB on V100 handles models exceeding 8 to 11 GB on GTX 1080, preventing out-of-memory errors during fine-tuning. In real-world terms, V100 accelerates LLM training by orders of magnitude while GTX 1080 suits lightweight inference.

Power efficiency tilts toward GTX 1080 at 180W TDP, yielding lower operational costs in small-scale deployments, though V100's interconnects like NVLink enhance multi-GPU scaling absent in GTX 1080's PCIe-only design.

The GTX 1080 excels in budget-constrained gaming or light ML inference where 8.9 TFLOPS FP32 suffices. Its 180W TDP and $0.30 per hour starting price make it ideal for hobbyist Stable Diffusion runs or legacy game servers, avoiding V100's higher average $0.94 per hour cost. Users with PCIe-only hosts prefer its single form factor compatibility.

The V100 dominates professional ML workflows needing 125 TFLOPS FP16 for rapid training. With 900 GB/s bandwidth and 16 to 32 GB VRAM, it supports large-batch LLM fine-tuning unavailable on GTX 1080. NVLink interconnect and 72 cloud offers from $0.10 per hour provide scalability for datacenter simulations.