GH200 vs GTX 1080

The GH200's FP16 performance reaches 1979 TFLOPS, dwarfing the GTX 1080's 8.9 TFLOPS: this disparity accelerates machine learning training and inference where half-precision computations dominate. The GH200's FP32 at 67 TFLOPS still exceeds the GTX 1080's 8.9 TFLOPS, but the larger FP16-to-FP32 ratio on the GH200 highlights its optimization for AI tensor operations over general graphics rendering.

Memory specifications define practical limits: the GH200's 96 GB HBM3 enables handling massive models that exceed the GTX 1080's 8 to 11 GB GDDR5X capacity, preventing out-of-memory errors in large language model training. Bandwidth at 4000 GB/s on the GH200 supports enormous batch sizes compared to 320 GB/s on the GTX 1080, reducing data loading bottlenecks and improving throughput in data-intensive tasks.

Power consumption underscores deployment differences: the GH200's 900W TDP demands data center infrastructure, while the GTX 1080's 180W suits edge or low-power setups. These factors translate to the GH200 enabling 200 times faster FP16 workloads, ideal for modern AI pipelines.

The GH200 excels in large-scale AI training and inference: its 96 GB HBM3 VRAM accommodates models over 70 billion parameters, and 1979 TFLOPS FP16 performance cuts training times dramatically. High memory bandwidth of 4000 GB/s allows massive batch sizes, optimizing throughput in cloud environments despite $3.59 per hour average cost.

Enterprise users benefit from NVLink-C2C interconnect for multi-GPU scaling, unavailable on the GTX 1080.

The GTX 1080 suits budget-conscious gaming or legacy applications: at $0.30 per hour starting price, it delivers 8.9 TFLOPS FP32 for real-time rendering without exceeding 180W TDP. Its PCIe form factor integrates easily into consumer workstations.

Light prototyping or non-AI tasks leverage its low cost, avoiding overkill from the GH200's capabilities.