A100 SXM4 40GB vs GTX 1080

The A100 SXM4 40GB dominates in compute throughput: its 312 TFLOPS FP16 vastly outpaces the GTX 1080's 8.9 TFLOPS, enabling faster AI training where half-precision is standard. FP32 performance shows 19.5 TFLOPS for the A100 versus 8.9 TFLOPS for the GTX 1080, benefiting single-precision scientific simulations. This delta means training large models on the A100 completes in fractions of the time required on the GTX 1080. Memory bandwidth of 2039 GB/s on the A100 supports massive batch sizes without bottlenecks, unlike the GTX 1080's 320 GB/s which limits datasets to smaller scales. For inference, the A100's 40 GB VRAM handles multiple concurrent requests, while the GTX 1080's 8 GB restricts it to modest deployments. Power draw reflects this: 400W TDP for the A100 versus 180W for the GTX 1080, influencing cloud costs for intensive runs.

Choose the NVIDIA A100 SXM4 40GB for demanding AI workloads: its 40 GB HBM2e VRAM and 2039 GB/s bandwidth excel in training large language models or fine-tuning with datasets exceeding 8 GB. NVLink and PCIe 4.0 interconnects enable multi-GPU scaling unavailable on the GTX 1080. At $1.00 to $2.63 per hour, it justifies the cost for production environments needing 312 TFLOPS FP16.

Select the NVIDIA GeForce GTX 1080 for budget-conscious light tasks: its $0.30 per hour pricing suits gaming, basic inference, or prototyping on small models fitting within 8 GB VRAM. The 180W TDP keeps operational costs low for intermittent use. It performs adequately at 8.9 TFLOPS FP32 for non-scaled scientific computing or Stable Diffusion at reduced resolutions.