GTX 1080 vs H200

The H200 vastly outperforms the GTX 1080 in compute capabilities: its FP16 reaches 1979 TFLOPS compared to 8.9 TFLOPS, and FP32 hits 67 TFLOPS against 8.9 TFLOPS. This gap means the H200 accelerates training of large neural networks by over 200 times in half-precision, critical for deep learning where FP16 dominates. The GTX 1080's equal FP16 and FP32 at 8.9 TFLOPS limits it to smaller models, as modern frameworks exploit mixed precision. For inference, H200's FP8 at 3958 TFLOPS further widens the lead, enabling low-latency serving of billion-parameter models. Memory specs amplify this: 141 GB HBM3e versus 8 to 11 GB GDDR5X allows H200 to process batch sizes hundreds of times larger without swapping, while 4800 GB/s bandwidth versus 320 GB/s reduces bottlenecks in data-heavy operations. In training scenarios, H200 supports larger batches for stable gradients; in inference, it handles concurrent requests efficiently. The GTX 1080 suits lightweight inference but struggles with contemporary model scales.

Choose the GTX 1080 for cost-sensitive, low-intensity workloads where 8.9 TFLOPS FP32 suffices. Its $0.30 per hour starting price and 180W TDP make it ideal for prototyping small models or legacy applications on PCIe form factors, avoiding the H200's 700W power draw. With only 2 cloud offers averaging $0.45 per hour, it fits budgets under $1 per hour for tasks not requiring over 11 GB VRAM.

Select the H200 for demanding AI tasks leveraging its 1979 TFLOPS FP16 and 141 GB VRAM. Datacenter features like NVLink, PCIe 5.0, and InfiniBand enable multi-GPU scaling unavailable on the GTX 1080's PCIe-only setup. Despite averaging $3.62 per hour across 26 offers, it delivers unmatched throughput for production-scale training and inference.