GH200 vs RTX 2080

Performance disparities dominate: GH200 achieves 1979 TFLOPS in FP16 and 3958 TFLOPS in FP8, dwarfing RTX 2080's 10.1 TFLOPS across FP16 and FP32. This FP16/FP32 delta on GH200, with 67 TFLOPS FP32 versus RTX 2080's matched 10.1 TFLOPS, accelerates deep learning training where tensor cores exploit half-precision for 196 times faster matrix multiplications. Inference benefits similarly, as FP8 precision on GH200 handles quantized models at scales impossible on RTX 2080. Memory bandwidth of 4000 GB/s on GH200 supports enormous batch sizes in transformer training, reducing I/O bottlenecks that plague RTX 2080's 616 GB/s. The 96 GB HBM3 versus 8-11 GB GDDR6 allows GH200 to load full LLMs like 70B-parameter models without sharding, while RTX 2080 limits to sub-7B models or heavy quantization. Power draw reflects intent: 900W TDP on GH200 powers sustained datacenter runs, contrasting 215W on RTX 2080 for edge efficiency. Real-world throughput scales exponentially with these specs, making GH200 viable for production AI versus RTX 2080's prototyping role.

Choose the GH200 for large-scale LLM training or inference requiring over 96 GB VRAM: its 1979 TFLOPS FP16 handles billion-parameter models at 4000 GB/s bandwidth for optimal batch sizes. Enterprise HPC simulations benefit from NVLink-C2C interconnects across SXM form factors, enabling multi-GPU clusters unavailable on RTX 2080. At $1.99 per hour average $3.59, it justifies costs for workloads exceeding RTX 2080's 10.1 TFLOPS and 8-11 GB limits.

Opt for RTX 2080 in budget-constrained prototyping or gaming: 10.1 TFLOPS FP32 suits small fine-tuning runs or Stable Diffusion at $0.05 per hour average $0.09. Its 215W TDP and PCIe form factor fit edge devices or personal rigs, avoiding GH200's 900W demands. Low VRAM of 8-11 GB works for sub-7B models where speed trumps scale.