H200 vs RTX 3080

The H200 delivers 989 TFLOPS in dense FP16 compared to 59.5 TFLOPS in dense FP16 on the RTX 3080. This gap means the H200 completes matrix operations in training and inference at a much higher rate when both use dense FP16 figures. The RTX 3080 reaches only 29.8 TFLOPS in FP32 while the H200 reaches 67 TFLOPS in FP32 so the H200 maintains a larger advantage in mixed precision paths. Memory bandwidth of 4800 GB/s on the H200 versus 760 GB/s on the RTX 3080 allows the H200 to support larger batch sizes without memory stalls during data movement. The H200 also lists 1979 TFLOPS in FP16 with sparsity while the RTX 3080 lists 119 TFLOPS in FP16 with sparsity so sparsity benefits scale similarly but from a higher base on the H200.

The H200 suits large scale LLM training because its 141 GB VRAM holds models that exceed the 12 GB limit of the RTX 3080. Its 4800 GB/s bandwidth supports high throughput in dense FP16 at 989 TFLOPS for sustained batch processing. Data center interconnects such as NVLink further favor the H200 in multi GPU scientific computing setups.

The RTX 3080 fits smaller inference tasks where 10 to 12 GB VRAM suffices and 320W TDP keeps power draw low. Its 59.5 TFLOPS in dense FP16 handles moderate Stable Diffusion workloads without the 700W requirement of the H200. PCIe form factor deployment remains simpler for single user environments.