GH200 Grace Hopper vs RTX 3080 Ti

Raw compute reveals a chasm in capabilities: the GH200's 1979 TFLOPS FP16 vastly outpaces the RTX 3080 Ti's 29.8 TFLOPS, enabling 66 times faster half-precision operations ideal for deep learning training. FP32 performance stands at 67 TFLOPS for the GH200 against 29.8 TFLOPS for the RTX 3080 Ti, providing over double the single-precision throughput for scientific simulations. FP8 at 3958 TFLOPS on the GH200 further accelerates quantized inference.

Memory specs dictate real-world usability: 96 GB HBM3 on the GH200 supports massive batch sizes in large language models, preventing out-of-memory errors common on the RTX 3080 Ti's 12 GB limit. Bandwidth of 4000 GB/s versus 760 GB/s ensures the GH200 sustains high data throughput, reducing bottlenecks in training loops. The RTX 3080 Ti suits smaller datasets where its 320W TDP enables efficient, low-cost operation, but it falters on memory-intensive workloads.

The GH200 excels in enterprise-scale AI training and inference: its 96 GB VRAM and 4000 GB/s bandwidth handle models exceeding 70 billion parameters with large batches. Users running LLM fine-tuning or scientific computing benefit from 1979 TFLOPS FP16, achieving results unattainable on consumer cards. Cloud deployments at $1.99 per hour justify the choice for production pipelines demanding NVLink interconnects.

The RTX 3080 Ti fits budget-conscious prototyping and lightweight inference: at $0.08 per hour, it processes Stable Diffusion or small fine-tuning tasks efficiently with 29.8 TFLOPS FP16. Its PCIe form factor and 320W TDP suit gaming rigs or edge deployments where 12 GB VRAM suffices for batches under 512. Developers avoid overprovisioning for non-datacenter needs.