The Energy Consumption of AI Models
Running large AI models, such as GPT, requires significant computational power and energy as demonstrated by the statistics above. These models are trained on vast datasets, which involves spinning up numerous servers and consuming substantial amounts of electricity. This not only has a negative impact on the environment but also incurs high costs for businesses if not controlled and managed effectively.
Ensuring this is discussed and considered during the model selection and deployment process helps to prevent unexpected surprises.
Choosing the Right Model for the Use Case
When it comes to implementing AI, one of the most critical decisions is selecting the right model for the specific use case. This is something we stress with all organisations implementing an AI strategy. This choice can significantly impact both the effectiveness, return on investment and the environmental footprint of the AI solution.
Here, we delve deeper into this topic, providing examples of different technologies from our key Data & AI partners, Microsoft and Databricks that can be leveraged for various use cases.
General-Purpose vs. Targeted Models
General-purpose models like GPT (Generative Pre-trained Transformer) are designed to handle a wide range of tasks by being trained on extensive datasets from diverse sources. These models are incredibly powerful but also resource-intensive, both in terms of computational power and energy consumption. For instance, GPT models are trained on the entire internet, making them suitable for tasks requiring broad general intelligence.
However, not all use cases require such extensive capabilities. For example, summarising customer complaints does not need the general intelligence of GPT. Instead, smaller, more targeted models can be more efficient and environmentally friendly.
By carefully selecting the right model for each use case, businesses can achieve their AI goals more efficiently and sustainably. Leveraging the specialised tools and platforms from partners such as Microsoft and Databricks can help ensure that AI initiatives are both cost-effective and environmentally responsible.
