H100 vs RTX 4080

The H100 dominates in AI-specific compute: its 1979 TFLOPS FP16 vastly exceeds the RTX 4080's 48.7 TFLOPS, accelerating mixed-precision training where FP16 predominates. The H100's FP32 at 67 TFLOPS edges the RTX 4080's balanced 48.7 TFLOPS, but the real delta appears in FP8 at 3958 TFLOPS on H100, ideal for inference quantization. This FP16 to FP32 ratio signals H100 optimization for deep learning forward passes over general compute. Memory bandwidth profoundly impacts workloads: H100's 3350 GB/s supports massive batch sizes in training large models, reducing iterations and time, while RTX 4080's 717 GB/s limits batches to smaller scales, risking out-of-memory errors beyond 16 GB VRAM. Power draw reflects intent: H100's 700W TDP suits enterprise cooling versus RTX 4080's efficient 320W for edge or desktop use. Interconnects further differentiate: H100's NVLink and PCIe 5.0 enable multi-GPU scaling, absent on RTX 4080.

Choose the H100 for large-scale LLM training or inference requiring over 16 GB VRAM. Its 80 to 94 GB HBM3 handles models like GPT variants without splitting, and 3350 GB/s bandwidth sustains huge batches. At 1979 TFLOPS FP16, it trains models 40 times faster than RTX 4080's 48.7 TFLOPS. Datacenter form factors like SXM5 and NVLink suit clustered deployments across InfiniBand.

Opt for RTX 4080 in budget-constrained prototyping or gaming-assisted tasks. Its $0.11 per hour minimum pricing undercuts H100's $0.80, with average $0.28 versus $3.14. The 16 GB GDDR6X suffices for fine-tuning small models or Stable Diffusion at 48.7 TFLOPS FP16, and 320W TDP fits low-power clouds. PCIe form factor simplifies single-node setups.