H200 SXM vs RTX 3080

The H200's FP16 performance of 1979 TFLOPS enables rapid deep learning training, where half-precision computations dominate: this exceeds the RTX 3080's 29.8 TFLOPS by over 66 times, accelerating model convergence on large datasets. Its FP32 rate of 67 TFLOPS suits simulation tasks better than the RTX 3080's matching 29.8 TFLOPS. FP8 at 3958 TFLOPS on the H200 optimizes low-precision inference, unavailable on the RTX 3080.

Memory defines scalability: 141 GB HBM3e on the H200 supports enormous batch sizes for training billion-parameter models, while 10-12 GB GDDR6X on the RTX 3080 restricts to smaller batches or model sharding. Bandwidth of 4800 GB/s versus 760 GB/s minimizes data bottlenecks, enhancing throughput in memory-intensive operations like transformer processing.

Power draw highlights deployment differences: the H200's 700W TDP demands robust cooling and infrastructure, contrasting the RTX 3080's efficient 320W. In practice, these specs position the H200 for production-scale AI and the RTX 3080 for prototyping or edge inference.

Enterprises tackling large-scale AI training select the H200: its 141 GB VRAM accommodates full models like 70B-parameter LLMs without partitioning, and 1979 TFLOPS FP16 speeds iterations. High bandwidth of 4800 GB/s sustains large batches, reducing time-to-results in cloud setups averaging $3.83 per hour.

Inference at scale favors the H200 too: 3958 TFLOPS FP8 handles high-query volumes efficiently, ideal for service deployments via NVLink or InfiniBand interconnects.

Budget-conscious developers choose the RTX 3080 for prototyping: 10-12 GB VRAM suffices for fine-tuning models under 7B parameters, with 29.8 TFLOPS FP16 matching FP32 for versatile experimentation at $0.06 per hour starting price.

Gaming or lightweight inference suits it best: 760 GB/s bandwidth and 320W TDP enable cost-effective runs of Stable Diffusion or small-batch serving without datacenter overhead.