Special Report: LLM vs. SLM Power Consumption – Why SLMs Are Better for Consumer Health Applications of AI, and What Will Change in AI for the Consumer in 2025

Special Report: LLM vs. SLM Power Consumption – Why SLMs Are Better for Consumer Health Applications of AI, and What Will Change in AI for the Consumer in 2025

By Noel J. Guillama-Alvarez

As we look ahead, the transformative potential of Artificial Intelligence (AI) in healthcare is undeniable. However, as AI tools like Large Language Models (LLMs) evolve, there are increasing concerns about their energy consumption and environmental impact. In this report, we explore why Small Language Models (SLMs) offer a more efficient and secure alternative for consumer-focused healthcare applications and consider the future of AI for consumers in 2025.

The Role of AI in Healthcare

AI, particularly LLMs like ChatGPT, Gemini, and others, has made a substantial impact on many industries, including healthcare. While LLMs show promise in areas like radiology, pathology, and genetics, they face significant challenges in healthcare. These challenges include issues around privacy, security, cost, and interoperability due to the complex landscape of over 3,000 exabytes of data in Electronic Health Records (EHRs) across 500+ platforms in the U.S. Healthcare providers are still grappling with these hurdles, making it difficult for AI to be fully integrated into mainstream healthcare systems.

However, consumers have a unique opportunity to take control of their own health data. By leveraging an SLM specifically designed for health and wellness, consumers can gain more transparency, make better-informed decisions, and communicate more effectively with their healthcare providers.

Why SLMs Are Better for Health and Wellness

A key argument for using Small Language Models (SLMs) over Large Language Models (LLMs) in consumer health applications comes down to several factors:

  • Energy Efficiency: SLMs require significantly less computational power compared to LLMs. LLMs, like OpenAI’s GPT-3, have trillions of parameters and require massive computing resources for training and inference. The energy demands are substantial, with some estimates suggesting that running models like ChatGPT can cost upwards of $700,000 per day in energy alone.
  • Cost Effectiveness: Due to their smaller size and focused architectures, SLMs are more energy-efficient, cheaper to deploy, and faster to deliver results. In contrast, LLMs can be prohibitively expensive for most applications outside of large-scale projects.
  • Security and Privacy: SLMs, by their nature, are more isolated from other data, which reduces the risk of cross-contamination from irrelevant or potentially harmful information. This makes them ideal for specialized applications in health and wellness, where privacy and security are paramount.
  • Specialization: While LLMs are versatile and capable of handling a wide range of tasks, they are not always necessary for highly specific tasks. SLMs, focused on specific domains, can provide better performance with fewer resources, without the distractions and inefficiencies of a general-purpose model.

The Environmental Impact of LLMs

The power consumption associated with training and operating LLMs is a growing concern. In fact, by 2040, the U.S. electricity demand due to AI models could increase by as much as 5.5%, according to a report from Columbia University’s Center on Global Energy Policy. This dramatic rise is tied to the ever-expanding complexity of AI models, which require more energy-intensive hardware to operate.

The environmental impact is also significant, with some studies estimating that training a single LLM could generate carbon dioxide emissions equivalent to the lifetime emissions of five cars. These concerns are amplified by reports like those from the World Economic Forum, which emphasize the need for energy-efficient models and infrastructure.

Small Language Models as a Solution

SLMs, by contrast, are purpose-built for specific tasks and consume far less energy than LLMs. They can handle healthcare tasks like analyzing EHR data, offering personalized health recommendations, or helping patients monitor their health more efficiently. For applications in health and wellness, where specialized knowledge and speed are essential, SLMs provide a more sustainable and effective approach.

Consumer Opportunities with AI

The rise of AI-powered consumer health tools is transforming the way people manage their health. From wearable devices that track blood glucose levels to at-home EKGs and sleep studies, consumers have more tools at their disposal than ever before. This marks a significant shift from the past when visits to the doctor were required for even basic health measurements.

In 2025, the trend toward consumer empowerment will continue to grow. The integration of AI into personal health management will allow consumers to monitor, consolidate, and analyze their health data in real-time. SLMs can help aggregate this information and provide actionable insights, enabling consumers to take control of their wellness journey.

By giving consumers the ability to secure and manage their own health data, AI can facilitate more informed decisions, leading to better health outcomes. Furthermore, SLMs will ensure that privacy and security remain intact, allowing for more trust in the AI tools they rely on.

Conclusion

In conclusion, as we look toward 2025, the use of AI in consumer health applications will continue to evolve. Small Language Models (SLMs), with their energy efficiency, cost-effectiveness, and security advantages, represent the future of AI in healthcare for consumers. By focusing on specialized, domain-specific models, we can harness the power of AI in a way that is not only more sustainable but also more impactful for improving individual health and wellness.

About HealthScoreAI™

Healthcare is at a tipping point, and HealthScoreAI is positioning to revolutionize the industry by giving consumers control over their health data and unlocking its immense value. U.S. healthcare annual spending has exceeded $5 trillion with little improvement in outcomes. Despite advances, technology has failed to reduce costs or improve care. Meanwhile, 3,000 exabytes of consumer health data remain trapped in fragmented USA systems, leaving consumers and doctors without a complete picture of care.

HealthScoreAI seeks to provide a unique solution, acting as a data surrogate for consumers and offering an unbiased holistic view of their health. By monetizing de-identified data, HealthScoreAI seeks to share revenue with consumers, potentially creating a new $100 billion market opportunity. With near-universal EHR adoption in the USA, and advances in technology, now is the perfect time to capitalize on the data available, practical use of AI and the empowering of consumers, in particular the 13,000 tech savvy baby boomers turning 65 every single day and entering the Medicare system for the first time.  Our team, with deep healthcare and tech expertise, holds U.S. patents and a proven track record of scaling companies and leading them to IPO.

Noel J. Guillama-Alvarez

https://www.linkedin.com/in/nguillama/

nguillama@mypwer.com

+1-561-904-9477, Ext 355

https://www.energypolicy.columbia.edu/projecting-the-electricity-demand-growth-of-generative-ai-large-language-models-in-the-us/

https://www.scientificamerican.com/article/the-ai-boom-could-use-a-shocking-amount-of-electricity/

https://www.wsj.com/articles/can-innovation-curb-ais-hunger-for-power-e9c3d8bc