H200 SXM vs RTX 4080

The H200's FP16 performance of 1979 TFLOPS accelerates deep learning training and inference far beyond the RTX 4080's 48.7 TFLOPS: its FP32 rate of 67 TFLOPS also exceeds the RTX 4080's matched 48.7 TFLOPS for general-purpose computing. FP8 capability at 3958 TFLOPS on the H200 further optimizes low-precision inference tasks common in production LLMs.

Memory specifications dictate real-world usability: H200's 141 GB VRAM supports enormous model sizes and batch sizes without offloading, enabled by 4800 GB/s bandwidth that minimizes data starvation. RTX 4080's 16 GB VRAM and 717 GB/s bandwidth constrain it to modest batches, often requiring model sharding or quantization for larger workloads.

Power and form factors reflect deployment contexts: H200's 700W TDP suits datacenter cooling with SXM and NVLink interconnects, while RTX 4080's 320W PCIe design fits consumer or edge setups. These differences translate to H200 enabling 10x larger scale in memory-bound training versus RTX 4080's efficiency in lightweight inference.

The H200 proves superior for large-scale LLM training and inference: 141 GB VRAM handles models over 70B parameters intact, with 4800 GB/s bandwidth supporting batch sizes exceeding thousands. FP16 at 1979 TFLOPS and FP8 at 3958 TFLOPS deliver throughput unattainable on consumer hardware.

Enterprise users prioritize H200 in cloud for production where interconnects like NVLink enable multi-GPU scaling across InfiniBand fabrics.

The RTX 4080 suits budget prototyping and fine-tuning small models: its $0.11 per hour starting price enables experimentation at 48.7 TFLOPS FP16 without high costs. 16 GB VRAM accommodates datasets under 10 GB effectively for tasks like image generation.

Gaming or creative workflows favor RTX 4080 due to 320W TDP and PCIe compatibility in smaller cloud instances.