The Carbon Footprint of GPUs: Balancing AI Performance and Sustainability  

FAQs

• What contributes to the carbon footprint of GPUs?

  The carbon footprint of GPUs is shaped by several factors that go beyond just direct electricity consumption. The primary contributors include:

   

  • Energy Consumption During Training and Inference: Training large AI models is highly energy-intensive, requiring thousands of GPU hours and consuming significant amounts of electricity. While inference consumes less power per task, it runs continuously in production systems, accounting for a large portion of total GPU energy demand in hyperscale data centres.
  • Manufacturing and Lifecycle Emissions: The environmental cost starts before a GPU is even used. This includes the extraction of raw materials, energy-demanding semiconductor fabrication, assembly, and global shipping. Lifecycle analysis also considers the complex disposal and recycling process for components like rare metals and circuit boards.
  • Cooling and Data Centre Infrastructure: GPUs generate substantial heat under heavy loads, requiring extensive cooling systems to maintain performance. In some data centres, cooling can account for up to 40% of the facility’s total energy use, significantly adding to the carbon footprint of GPU workloads.
• How is the NVIDIA H100 GPU designed for better energy efficiency?

  The NVIDIA H100 Tensor Core GPU was designed with features to improve its performance-per-watt and reduce its carbon impact. Its efficiency comes from:

   

  • Architectural Improvements: The H100 features advanced packaging and refined Tensor Core operations that increase efficiency in matrix multiplications, which are central to most AI workloads. These changes help the H100 deliver approximately three times the performance-per-watt of the previous A100 GPU.
  • Operational Controls: It supports methods like power capping, allowing operators to set a maximum wattage, and dynamic voltage and frequency scaling (DVFS), which adjusts the GPU’s performance in real time to match workload demand, thus optimising power consumption.
  • Workload Optimisation: The H100 supports Multi-Instance GPU (MIG) technology, which partitions a single physical GPU into smaller, isolated instances. This allows multiple workloads to run in parallel, maximising utilisation and reducing energy wasted on idle cycles.
• What is the scale of carbon emissions from training large AI models?

  Training large-scale AI models, such as language models with hundreds of billions of parameters, requires immense computational power and generates a substantial carbon footprint. Studies have shown that the process can emit hundreds of tons of CO₂, an amount comparable to the annual carbon footprint of hundreds of cars. This high level of emissions is due to the need for thousands of GPUs to run uninterrupted for weeks or even months, with each GPU consuming between 300 and 700 watts under full load.

• How does the carbon footprint of AI training differ between academic and enterprise settings?

  There is a notable distinction between the carbon footprint of academic research and that of enterprise-scale deployments.

   

  • Academic research typically involves fewer GPUs and smaller clusters due to budget limitations. This results in lower absolute emissions, but the training process may take longer and be less efficient.
  • Enterprise deployments, by contrast, use vast GPU clusters to train models quickly to meet market demands. While they may use efficient modern GPUs like the H100, the sheer scale of these operations means their absolute emissions are much higher.
• How do data centres and cooling systems contribute to the carbon footprint of GPUs?

  Data centres and their cooling systems play a critical role in the overall carbon footprint of GPU-based workloads. High-performance GPUs generate significant heat, and cooling systems can account for as much as 40% of a data centre’s total energy consumption. To manage this, operators are adopting advanced techniques like liquid and immersion cooling, which are more efficient than traditional air cooling. Major cloud providers such as AWS, Google, and Microsoft are investing in these advanced cooling solutions and are committed to powering their data centres with renewable energy to reduce their carbon footprint.