Green Computing

Whether you’re a scientist working on climate modeling, an engineer designing new products, or a data analyst making sense of large datasets, NVIDIA’s solutions can help you do your life’s work better and more efficiently.

Use this simple tool to compare the costs and energy use of a workload running on an x86 CPU-based server versus an NVIDIA GPU-accelerated server. You’ll see:

• A node count comparison for equal throughput
• The annual energy and cost savings for each system at equal throughput
• Estimates of CO₂-equivalent savings

To use it, you’ll need to know:

• The type of NVIDIA GPU you have,
• The number of GPUs,
• The software application, and
• The dataset (model of interest)

If you’d like to estimate the savings for an application not on our list, you’ll need to calculate the Node Factor Replacement (NRF). NRF is the number of CPU-only servers replaced by a single GPU-accelerated server. Alternatively, NRF is the number of CPU servers required to provide equivalent throughput to a single GPU server. NRF will vary by application.

For additional comparisons, see the AI and high-performance computing (HPC) performance pages for Training, Inference, and HPC.