A100 SXM4 80GB vs GTX 1080

The A100 demonstrates overwhelming superiority in compute throughput: its FP16 performance hits 312 TFLOPS, enabling rapid mixed-precision training for large neural networks, while FP32 stands at 19.5 TFLOPS for precise scientific simulations. The GTX 1080 matches FP16 and FP32 at only 8.9 TFLOPS each, limiting it to smaller models or inference on modest datasets. This FP16/FP32 delta means the A100 accelerates training by leveraging tensor cores for AI workloads, whereas the GTX 1080 lacks such optimizations and struggles with memory-intensive operations.

Memory bandwidth profoundly impacts real-world usage. The A100's 2039 GB/s supports massive batch sizes in training, reducing per-iteration time for models exceeding 8 GB VRAM, such as large language models. The GTX 1080's 320 GB/s bandwidth constrains batch sizes, often requiring gradient accumulation and slowing convergence. Higher TDP of 400W on the A100 versus 180W on the GTX 1080 reflects its capacity for sustained high loads in multi-GPU clusters via NVLink, unlike the single PCIe GTX 1080.

The A100 excels in demanding AI training and inference scenarios requiring over 40 GB VRAM, such as fine-tuning billion-parameter LLMs where its 80 GB HBM2e prevents out-of-memory errors. High memory bandwidth of 2039 GB/s enables large batch sizes, speeding up iterations in distributed setups with NVLink and PCIe 4.0. Cloud users benefit from its 312 TFLOPS FP16 for production-scale workloads despite higher average pricing of $1.39 per hour.

The GTX 1080 fits budget-conscious prototyping or gaming where costs must stay under $0.30 per hour. Its 8 GB GDDR5X suffices for lightweight inference on models under 4 GB or Stable Diffusion at low resolutions, with 8.9 TFLOPS FP32 matching everyday compute needs. Lower 180W TDP suits edge deployments or single-node tests without advanced interconnects.