H200 NVL vs RTX 4080 SUPER

The H200 NVL dominates in raw compute with 1979 TFLOPS FP16 and 3958 TFLOPS FP8, making it ideal for accelerating AI training and inference on massive models, while the RTX 4080 SUPER's 48.7 TFLOPS FP16 and FP32 limits it to smaller workloads. The H200 NVL's FP32 at 67 TFLOPS edges out the RTX 4080 SUPER's 48.7 TFLOPS, providing better precision for scientific simulations, but the consumer card's balanced FP16 and FP32 suits graphics rendering equally well.

Memory specs reshape real-world usage: 141 GB HBM3e VRAM on the H200 NVL supports enormous batch sizes without swapping, whereas 16 GB GDDR6X on the RTX 4080 SUPER constrains large language models to modest batches. The 4800 GB/s bandwidth of the H200 NVL minimizes data movement bottlenecks in training loops, enabling 6.7 times faster throughput than the RTX 4080 SUPER's 717 GB/s. For inference, FP8 on the H200 NVL doubles effective speed over FP16, a capability absent in the RTX 4080 SUPER.

Select the H200 NVL for enterprise AI training where models exceed 70 billion parameters, leveraging its 141 GB VRAM and NVLink interconnect for multi-GPU scaling across InfiniBand clusters. Its 1979 TFLOPS FP16 and 4800 GB/s bandwidth handle large batch sizes efficiently, reducing training times in cloud setups averaging $2.39 per hour.

High-throughput inference on FP8 at 3958 TFLOPS makes it superior for production LLMs serving thousands of queries.

The RTX 4080 SUPER excels in budget-conscious prototyping or gaming-integrated workflows, with 16 GB VRAM sufficient for models under 13 billion parameters at $0.17 per hour starting price. Lower 320W TDP suits edge deployments or shared cloud instances without high power demands.

For Stable Diffusion or real-time graphics, its balanced 48.7 TFLOPS FP16 and FP32 deliver responsive performance at an average $0.32 per hour.