GH200 vs RTX 2080

The GH200 delivers 989 TFLOPS dense FP16 while the RTX 2080 delivers 40.3 TFLOPS dense FP16. This difference means the GH200 supports substantially larger batch sizes during training and inference operations that rely on dense FP16 computations. Memory bandwidth of 4000 GB/s on the GH200 versus 448 GB/s on the RTX 2080 further enables the GH200 to handle bigger data movements without bottlenecks. The GH200 also provides 1979 TFLOPS with sparsity in FP16 compared to the RTX 2080 dense FP16 figure of 40.3 TFLOPS. FP32 workloads show the GH200 at 67 TFLOPS against 10 TFLOPS on the RTX 2080 which affects precision sensitive scientific tasks.

The GH200 suits scenarios that require 96 GB HBM3 VRAM and 989 TFLOPS dense FP16 for large scale model operations. Its 4000 GB/s memory bandwidth supports high throughput in environments where the 900W TDP is acceptable. Users select the GH200 when the 67 TFLOPS FP32 rating addresses intensive computational demands.

The RTX 2080 fits tasks where 215W TDP and 8-11 GB GDDR6 VRAM suffice for smaller workloads. Its 10 TFLOPS FP32 performance meets basic requirements without the higher power draw of 900W. The 40.3 TFLOPS dense FP16 rating works for lighter inference or development activities.