GTX 1080 Ti vs H100 NVL

Compute specifications reveal stark disparities suited to different workloads. The GTX 1080 Ti delivers 8.9 TFLOPS in both FP16 and FP32, adequate for basic floating-point operations but limited for deep learning due to absent tensor cores. The H100 NVL achieves 1979 TFLOPS FP16 and 67 TFLOPS FP32: this enables training acceleration by over 200 times in half-precision tasks common in neural networks. FP8 performance at 3958 TFLOPS further optimizes inference for large language models. Memory bandwidth defines practical limits on data throughput: 320 GB/s on the GTX 1080 Ti restricts batch sizes in memory-intensive training, often causing out-of-memory errors beyond small models, whereas 3350 GB/s on the H100 NVL supports massive batches and reduces latency. VRAM capacity amplifies this: 8-11 GB handles modest datasets on the GTX 1080 Ti, but 80-94 GB fits entire large models on the H100 NVL, minimizing data swapping. Higher TDP of 700W reflects datacenter scaling, yet efficiency gains from Hopper architecture yield superior performance per watt.

The GTX 1080 Ti suits budget-conscious users with light workloads. It excels in legacy gaming, basic computer vision inference, or prototyping where 8.9 TFLOPS FP32 and 8-11 GB VRAM suffice at $0.60 per hour. Low 180W TDP fits edge deployments or small-scale testing without high power costs.

The H100 NVL dominates demanding AI pipelines. Choose it for LLM training or inference requiring 1979 TFLOPS FP16, 3958 TFLOPS FP8, and 80-94 GB VRAM to process vast datasets at 3350 GB/s bandwidth. NVLink interconnects enable multi-GPU scaling despite $2.89 per hour average pricing.