H200 NVL vs RTX 4080

Memory capacity defines a core divide: the H200 NVL's 141 GB HBM3e supports model sizes and batch processing far beyond the RTX 4080's 16 GB GDDR6X limit. Bandwidth reinforces this at 4800 GB/s for H200 NVL compared to 717 GB/s on RTX 4080, enabling larger batch sizes in training and reducing data loading bottlenecks during inference. FP16 performance on H200 NVL achieves 1979 TFLOPS, which accelerates mixed-precision training common in deep learning, while RTX 4080 manages 48.7 TFLOPS and suits smaller-scale tensor operations. The H200 NVL's FP32 at 67 TFLOPS edges out RTX 4080's 48.7 TFLOPS for simulation tasks requiring single-precision compute. FP8 capability on H200 NVL reaches 3958 TFLOPS, optimizing quantized inference for large language models where RTX 4080 lacks equivalent support. Power draw differs at 700W for H200 NVL versus 320W for RTX 4080, impacting density in multi-GPU setups.

Enterprises select the NVIDIA H200 NVL for large-scale LLM training: its 141 GB VRAM accommodates models exceeding 100 billion parameters, and 1979 TFLOPS FP16 speeds convergence. Datacenter inference benefits from 4800 GB/s bandwidth for high-throughput serving at scale. NVLink and InfiniBand interconnects enable multi-GPU clustering unavailable on RTX 4080.

Budget-conscious users or developers choose the NVIDIA GeForce RTX 4080 for prototyping: 16 GB VRAM handles fine-tuning of models under 7 billion parameters, and pricing from $0.11 per hour fits experimentation. Stable Diffusion and gaming workloads leverage its 48.7 TFLOPS FP16 at 320W TDP for efficient single-GPU use without datacenter overhead.