GTX 1080 vs H200 SXM

H200's FP16 performance of 1979 TFLOPS vastly outpaces GTX 1080's 8.9 TFLOPS, accelerating AI training where half-precision computations dominate and enabling models infeasible on older hardware. In FP32, H200's 67 TFLOPS supports scientific simulations over 7 times faster than GTX 1080's 8.9 TFLOPS. The FP16 to FP32 ratio on H200 favors mixed-precision training, unlike GTX 1080's parity which limits efficiency in modern frameworks.

Memory differences profoundly impact workloads: H200's 4800 GB/s bandwidth and 141 GB VRAM allow enormous batch sizes in LLM inference, processing datasets without swapping, while GTX 1080's 320 GB/s and 8 GB to 11 GB VRAM constrain users to small models or low batches. This bandwidth edge reduces training epochs on H200 by facilitating data-parallel scaling. Higher 700W TDP on H200 enables dense compute but demands robust cooling, contrasting GTX 1080's efficient 180W for lighter deployments.

Interconnects further differentiate: H200 supports NVLink, PCIe 5.0, and InfiniBand for multi-GPU clusters, versus GTX 1080's basic PCIe.

Select the GTX 1080 for budget-sensitive, low-intensity tasks like gaming, lightweight image generation, or hobbyist ML inference on models under 8 GB. Its $0.30 per hour pricing and 180W TDP make it ideal for personal projects or edge computing where 8.9 TFLOPS FP32 suffices and power efficiency matters.

Choose the H200 SXM for demanding AI pipelines: 141 GB VRAM loads full-scale LLMs for training or inference, with 1979 TFLOPS FP16 slashing iteration times. Despite $1.19 per hour starting costs, 4800 GB/s bandwidth justifies it for production-scale batch processing and multi-node scaling via NVLink.