GH200 vs T4

The GH200's FP16 performance of 1979 TFLOPS dwarfs the T4's 8.1 TFLOPS, enabling training of massive neural networks in hours rather than days on the T4. This disparity accelerates mixed-precision training, where FP16 handles most computations while FP32 at 67 TFLOPS on GH200 maintains precision for gradients, outperforming T4's equal 8.1 TFLOPS across both.

Memory bandwidth of 4000 GB/s on the GH200 permits much larger batch sizes than the T4's 320 GB/s, reducing per-iteration time and improving throughput in memory-bound tasks like transformer models. For inference, GH200's FP8 capability at 3958 TFLOPS supports ultra-efficient quantized serving, far beyond T4's limits. Power draw underscores efficiency differences: GH200 at 900W TDP suits data centers, while T4's 70W enables dense deployments.

Real-world impact appears in scalability: GH200's NVLink-C2C and PCIe 5.0 interconnects facilitate multi-GPU clusters, unlike T4's basic PCIe, enhancing distributed training speed by minimizing communication bottlenecks.

Select the GH200 for large-scale LLM training or fine-tuning, where 96 GB HBM3 VRAM and 1979 TFLOPS FP16 handle models exceeding 70 billion parameters without issues. Its 4000 GB/s bandwidth supports batch sizes 10 times larger than feasible on T4, cutting training time dramatically. High-performance computing tasks benefit from FP8 inference at 3958 TFLOPS and SXM form factor for rack-scale deployments.

Choose the T4 for cost-sensitive inference on smaller models, leveraging its $0.53 per hour starting price and 70W TDP for low-power environments like edge servers. The 16 GB GDDR6 suffices for serving models under 7 billion parameters at 8.1 TFLOPS FP16. Multi-GPU setups gain from PCIe form factor and six cloud offers averaging $1.66 per hour, ideal for high-density, budget deployments.