A100 PCIe 80GB vs GTX 1080 Ti

The A100 PCIe 80GB's FP16 performance of 312 TFLOPS towers over the GTX 1080 Ti's 8.9 TFLOPS, translating to up to 35 times faster mixed-precision training for deep learning models. This delta accelerates neural network optimization where half-precision computations dominate, reducing training times from days to hours on large datasets. FP32 performance at 19.5 TFLOPS on the A100 PCIe 80GB exceeds the GTX 1080 Ti's 8.9 TFLOPS, benefiting scientific computing and simulations requiring full single-precision accuracy.

Memory bandwidth defines practical limits: the A100 PCIe 80GB's 2039 GB/s supports massive batch sizes in model training, preventing stalls during data loading for LLMs with billions of parameters. The GTX 1080 Ti's 320 GB/s restricts it to smaller batches, often necessitating gradient accumulation and slowing convergence. VRAM disparity, 80 GB versus 11 GB, allows the A100 PCIe 80GB to load entire large models solo, minimizing multi-GPU overhead and communication via NVLink, absent on the GTX 1080 Ti.

Choose the NVIDIA A100 PCIe 80GB for large-scale AI training and inference where 80 GB HBM2e VRAM and 312 TFLOPS FP16 handle models exceeding 11 GB, such as LLMs or diffusion models. Its 2039 GB/s bandwidth sustains high throughput in data center environments with NVLink interconnects. Cloud users benefit from 28 live offers starting at $0.89/hr for production workloads demanding speed over cost.

High TDP of 400W suits optimized clusters, outperforming the GTX 1080 Ti in fine-tuning or scientific computing by leveraging 19.5 TFLOPS FP32.

The NVIDIA GeForce GTX 1080 Ti fits budget-conscious tasks like lightweight inference or gaming emulation, with 11 GB GDDR5X VRAM sufficient for models under 8 GB and pricing at $0.60/hr. Lower 180W TDP reduces power costs in small-scale setups without NVLink needs.

It excels in legacy Pascal-optimized software or simple Stable Diffusion runs where 8.9 TFLOPS FP16 provides adequate speed without the A100 PCIe 80GB's overhead.