GH200 vs RTX 4080

The GH200's FP16 performance of 1979 TFLOPS vastly exceeds the RTX 4080's 48.7 TFLOPS, accelerating low-precision training and inference in deep learning models. Its FP32 rate of 67 TFLOPS slightly outpaces the RTX 4080's 48.7 TFLOPS, but the imbalance underscores Hopper's optimization for AI over general compute. The RTX 4080's equal FP16 and FP32 figures suit graphics and balanced workloads.

Memory specifications define real-world limits: the GH200's 96 GB HBM3 at 4000 GB/s supports enormous batch sizes for training large language models, minimizing data transfer bottlenecks. The RTX 4080's 16 GB GDDR6X at 717 GB/s restricts it to smaller models or reduced batches, increasing iteration times. This gap proves critical in memory-bound tasks like transformer training.

Power draw reflects deployment scales: the GH200's 900W TDP demands data center cooling, while the RTX 4080's 320W fits edge or multi-GPU consumer setups. Bandwidth dominance enables the GH200 to process datasets 5.6 times faster, enhancing throughput in inference serving.

Enterprises training billion-parameter LLMs select the GH200 for its 96 GB VRAM and 4000 GB/s bandwidth, accommodating full model loading without fragmentation. High FP16 performance of 1979 TFLOPS and FP8 at 3958 TFLOPS speed mixed-precision workflows on massive datasets.

Scientific simulations or multi-node clusters leverage NVLink-C2C interconnects and PCIe 5.0, where the RTX 4080's 16 GB limits scale.

Budget-conscious developers fine-tuning small models or running Stable Diffusion opt for the RTX 4080 at $0.11 per hour average, as 16 GB VRAM and 48.7 TFLOPS FP16 suffice for sub-10B parameter tasks. Its 320W TDP enables dense cloud instances without high cooling costs.

Gaming, rendering, or prototyping benefit from PCIe accessibility and low entry pricing versus the GH200's $3.59 average.