GH200 vs RTX 3080

The FP16 dense performance of 989 TFLOPS on the GH200 exceeds the 59.5 TFLOPS dense figure on the RTX 3080 by a ratio of approximately 16.6 to 1 which translates to substantially higher throughput for training and inference operations that rely on dense tensor computations. When sparsity is enabled the GH200 reaches 1979 TFLOPS FP16 with sparsity against 119 TFLOPS FP16 with sparsity on the RTX 3080 maintaining the same proportional advantage for workloads that can exploit sparsity. Memory bandwidth of 4000 GB/s on the GH200 versus 760 GB/s on the RTX 3080 directly supports larger batch sizes during training because data movement between memory and compute units occurs at a higher rate without creating bottlenecks. The FP32 delta of 67 TFLOPS versus 29.8 TFLOPS further indicates that the GH200 handles precision sensitive scientific calculations with greater efficiency than the RTX 3080.

The GH200 suits scenarios that require 96 GB HBM3 VRAM and 4000 GB/s bandwidth such as training large language models or running scientific simulations that exceed the 10 to 12 GB capacity of the RTX 3080. Its 989 TFLOPS FP16 dense rating and 67 TFLOPS FP32 rating provide the numerical headroom needed for dense tensor operations at scale while the NVLink C2C interconnect enables multi GPU scaling not available on the PCIe 4.0 interface of the RTX 3080.

The RTX 3080 fits workloads that remain within 10 to 12 GB GDDR6X VRAM limits and operate at lower power draw of 320W such as inference on compact models or fine tuning smaller networks where the 59.5 TFLOPS FP16 dense performance meets requirements without the 900W TDP of the GH200. Its PCIe form factor and 760 GB/s bandwidth support consumer grade deployments where the higher density and power of the GH200 would be unnecessary.