H200 SXM vs RTX 4080 SUPER

The H200's FP16 performance of 1979 TFLOPS vastly outpaces the RTX 4080 SUPER's 48.7 TFLOPS, making it ideal for machine learning training where half-precision computations dominate and accelerate convergence on large datasets. In contrast, FP32 performance shows the H200 at 67 TFLOPS slightly ahead of the RTX 4080 SUPER's 48.7 TFLOPS, but the H200's FP8 capability of 3958 TFLOPS enables ultra-efficient inference for quantized models. These metrics translate to the H200 handling model training epochs far quicker in real-world scenarios.

Memory differences profoundly affect usability: the H200's 141 GB HBM3e VRAM and 4800 GB/s bandwidth support massive batch sizes in transformer models, preventing out-of-memory errors common with the RTX 4080 SUPER's 16 GB and 717 GB/s. For inference, higher bandwidth reduces latency in serving high-throughput requests. Power draw reflects this: 700W TDP for H200 versus 320W for RTX 4080 SUPER, necessitating robust cooling in datacenters but allowing efficient consumer deployment.

The H200 excels in large-scale AI training and inference for LLMs exceeding 70B parameters, leveraging 141 GB VRAM to fit entire models without sharding. Datacenter environments benefit from its NVLink interconnect and 4800 GB/s bandwidth for multi-GPU scaling across clusters. Cloud users prioritizing FP16 at 1979 TFLOPS choose H200 for production workloads despite $1.19 to $3.78 hourly costs.

The RTX 4080 SUPER suits budget-conscious users for Stable Diffusion or fine-tuning smaller models under 7B parameters, fitting within 16 GB VRAM at $0.17 per hour average. Gaming, video editing, or lightweight inference benefit from its 320W efficiency and PCIe form factor in single-node setups. It delivers solid 48.7 TFLOPS FP16 for prosumer tasks without datacenter overhead.