There’s always going to be the notion with technology that bigger is better. The more powerful, the more capable, the more sophisticated – these are the qualities tied to larger and more complex systems. Yet, when it comes to generative AI models, the story isn’t as straightforward. There’s an interesting David versus Goliath dynamic at play between big and small generative AIs. Let me explain…
In recent years as we know, big generative AI models have garnered tons of attention and acclaim. Models like ChatGPT-3, with its 175 billion parameters, GPT 4 with an estimated 170 trillion parameters, or Midjourney with its large language and diffusion models as well as its comprehensive list of parameters; these models have demonstrated remarkable capabilities and can pretty much generate anything you want. They’re trained on massive amounts of data, that allow them to capture complex patterns and produce the outputs we value so much. Why is that important to know? Well, it’s instances like this that have earned big generative models such a grand reputation.
On the other hand, we have smaller generative AIs. Right off the bat, these models have fewer parameters and less computational power, which might make them seem like underdogs compared to their larger counterparts. But make no mistake; they possess unique advantages that make them fierce competitors in the AI landscape.
Generative AIs in Action
One of the most notable advantages when it comes to small generative AIs is their efficiency. Due to their smaller size, they require less computational resources and can be deployed on devices that have limited processing power. This makes them ideal for applications that require real-time generation or that have strict resource constraints.
Think about a mobile app that generates customized images based on user prompts. Since a small generative AI doesn’t have to depend on a remote server, it can process prompts directly on the user's device. This eliminates the need for constant internet connectivity and reduces latency, resulting in a super responsive user experience. When you compare this to a big generative AI, it’s not as powerful in terms of the sheer scale and volume of outputs. However, a small generative AI model, in this case, embedded in a mobile app is independent, efficient, secure, and highly customizable which makes it a versatile tool, especially for something such as personalized image generation.
Where One Compliments the Other
Using a big generative AI, like that of ChatGPT does offer a lot more in terms of capabilities. If a multi-billion dollar corporation sat down and decided they wanted to develop an AI system with immense scale and resources that was going to revolutionize healthcare (For instance) a big generative AI model combined with a small generative AI model would be the ideal solution.
First off, the big generative AI model would be trained on vast amounts of medical data. With its scale and resources, it will capture complex patterns and relationships within the data, enabling it to provide advanced diagnostic support, predict outcomes, and assist in drug discovery and development.
However, deploying a system like this is going to require a lot of computational power and an infrastructure that can handle the sheer scale of data being processed. This is where the small generative AI model comes into play.
The small generative AI model is now embedded within medical devices, wearables, and mobile applications, which again, enables data processing in real-time. With that, it now analyzes patient-specific data, such as vital signs, symptoms, and lifestyle factors, to provide immediate feedback, personalized recommendations, and continuous monitoring.
Don't Compete - Balance and Complete
To break this down simply, the big model is the brain that processes and stores the information, and the small model is the hands that carry out the actionable. Achieving a balance between the two is simply leveraging the strengths of each and coordinating to ensure data can be exchanged easily between models. How do you enforce this? By following these 4 guidelines:
Each model has clear tasks
Protocols are in place to facilitate data exchange
Workloads are distributed based on computational requirements
The system is constantly monitored and being improved on
These actionables are very general and could be applied to any industry but they give you a sense of what it takes to achieve balance and coordination between big and small generative AI models. While the specific implementation may vary across industries, these general guidelines provide a framework for companies to start with.
The Results Generative AIs Deliver in a Business
Big or small, generative AIs deliver results, however, the size and complexity of the model will be a big factor in determining the quality of results attained. From what we know about big generative models, we know that they excel at generating high-quality content, predicting trends, optimizing systems, and driving innovation. By that same token with small models, we know their efficiency offers agility, responsiveness, and personalized experiences. They’re awesome for tasks such as personalized recommendations, interactive applications, and ultimately enhancing customer engagement.
The Takeaway
While big generative AI models have garnered attention for their remarkable capabilities and ability to generate high-quality content, small generative AI models shouldn't be underestimated. Businesses have a lot to gain by leveraging each but ultimately it comes down to the strategy you put behind them.
Written By Ben Brown
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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
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