A100 PCIe 80GB vs GTX 1080

The A100's FP16 performance of 312 TFLOPS vastly outpaces the GTX 1080's 8.9 TFLOPS, accelerating mixed-precision training where models use half-precision for speed: training times shorten dramatically for deep learning networks. Its FP32 rate of 19.5 TFLOPS exceeds the GTX 1080's 8.9 TFLOPS, benefiting single-precision inference and simulations requiring full accuracy.

Memory bandwidth disparity proves critical: the A100's 2039 GB/s supports larger batch sizes in training, reducing overhead and enabling models up to 80 GB, while the GTX 1080's 320 GB/s limits batches to smaller datasets, risking out-of-memory errors on complex tasks. This affects real-world throughput, as higher bandwidth minimizes stalls during data loading in inference pipelines.

Power draw underscores efficiency gaps: the A100's 400W TDP sustains peak performance in clusters via NVLink and PCIe 4.0, unlike the GTX 1080's 180W PCIe-only setup suited for single-node hobbyist use.

Select the A100 PCIe 80GB for large-scale AI training or inference demanding over 40 GB VRAM, such as transformer models where its 80 GB HBM2e prevents swapping. Its 312 TFLOPS FP16 excels in accelerating LLM fine-tuning, cutting epochs from days to hours compared to the GTX 1080's constraints. Cloud deployments benefit from 28 pricing options starting at $0.89 per hour.

Choose the GTX 1080 for budget-conscious prototyping or lightweight inference on models under 8 GB, where its $0.30 per hour cost across available offers minimizes expense. It suffices for basic Stable Diffusion runs or small-batch fine-tuning at 8.9 TFLOPS FP16, avoiding overkill on low-TDP 180W setups. Hobbyists value its simplicity for non-enterprise tasks.