GTX 1080 vs H100 PCIe

The H100 PCIe vastly outperforms the GTX 1080 in compute capabilities: 1979 TFLOPS FP16 compared to 8.9 TFLOPS, and 67 TFLOPS FP32 against 8.9 TFLOPS. This delta accelerates AI training where FP16 tensor cores dominate, enabling the H100 to process models 222 times faster in half-precision. FP32 parity on GTX 1080 limits it to legacy single-precision tasks, while H100's FP8 at 3958 TFLOPS suits quantized inference.

Memory specifications transform workloads. H100's 80 to 94 GB HBM3 and 3350 GB/s bandwidth support massive batch sizes in deep learning, reducing data movement bottlenecks that plague GTX 1080's 8 to 11 GB GDDR5X at 320 GB/s. Larger VRAM on H100 handles models exceeding 70 GB, impossible on GTX 1080 without severe fragmentation.

Power draw reflects scaling: 700W TDP for H100 demands robust cooling versus 180W for GTX 1080. Interconnects like NVLink on H100 enable multi-GPU scaling for distributed training, outperforming GTX 1080's basic PCIe.

The GTX 1080 suits budget-conscious users for light gaming, video editing, or basic machine learning prototypes. At $0.30 per hour average across one offer, it provides 8.9 TFLOPS FP32 for tasks fitting within 8 to 11 GB VRAM and 320 GB/s bandwidth. Its 180W TDP fits low-power cloud instances without high costs.

Choose GTX 1080 when workloads avoid large models or high-precision AI, such as older games or simple inference on small datasets.

The H100 PCIe excels in demanding AI and HPC applications requiring 80 to 94 GB VRAM and 3350 GB/s bandwidth. Renters access it from $1.25 per hour across 16 offers, averaging $2.68, justified by 1979 TFLOPS FP16 for rapid training and 3958 TFLOPS FP8 for inference.

Select H100 for scalable setups using NVLink or PCIe 5.0, where 700W TDP supports intensive compute unattainable by GTX 1080.