The emergence of generative artificial intelligence (AI) has taken the business world by storm. Initially seen as a novelty, it has now evolved into a technology with significant implications for various industries. Major players such as MSFT, AWS, and Google are engaged in an intense “AI arms race” to establish dominance. Enterprises are rapidly pivoting to avoid being left behind or missing out on valuable opportunities. Moreover, venture capitalists are fueling the growth of new companies powered by large language models (LLMs). However, with any new technology, challenges arise, including model veracity, bias, cost of training, and even issues related to identity and security.
Open-Source vs. Closed-Source Debate
The open-source versus closed-source debate has resurfaced with the advent of generative AI. While both have their merits, open-source models offer advantages such as lower deployment costs, accessibility, and flexibility. However, the availability of open-source models has outpaced the progress in the development of technologies to effectively deploy them. This mismatch presents a challenge for businesses seeking to leverage these models in a practical and viable manner.
The High Cost of Running Large Models
One of the most pressing concerns regarding generative AI is the exorbitant cost of running large models in production. Generative models are complex and computationally intensive, making them significantly more expensive to operate compared to other types of machine learning models. To illustrate this, let’s consider the example of a home décor app that utilizes the Stable Diffusion model to help customers visualize their rooms in different design styles. While the cost may seem reasonable initially, it quickly becomes astronomical when the app goes viral and attracts millions of active daily users. Inference costs can escalate to millions of dollars per year, posing a major threat to innovation and profitability.
Despite the significant advancements made by proprietary models in a short period, they are not the only viable option. Open-source models show great promise in terms of flexibility, performance, and cost savings. These models offer a practical alternative for emerging companies looking to leverage generative AI without succumbing to the financial burden associated with proprietary models. Companies that adopt a hybrid approach, integrating both proprietary and open-source models, can strike a balance that optimizes costs and performance.
The Rise of Open-Source Models
Open-source models are gaining traction due to their flexibility and versatility. They can be deployed on various hardware platforms with the right tooling, providing developers with greater control and customization options. Existing examples such as Meta’s LLaMA 2 and Databricks’ Dolly showcase the potential and affordability of open-source models. With minimal retraining costs, these models can be run on everyday devices such as Macbook Pros, smartphones, or even Raspberry Pis. This democratization of generative AI empowers developers and promotes innovation on a global scale.
Drawing lessons from the open-source software community, embracing open-source models can foster a collaborative environment of developers, researchers, and innovators. By making AI models openly accessible, the potential for innovation is amplified. It enables customization and improvement of existing models for the greater good. The ability to choose between open-source, off-the-shelf, or custom models empowers developers to find the best fit for their specific needs. In such a world, the possibilities for generative AI are truly limitless.
Generative AI has moved beyond the realm of novelty and entertainment and is making a profound impact on businesses worldwide. As the AI arms race intensifies, companies must carefully consider the cost implications of running large models in production. While proprietary models have made significant strides, open-source models offer a viable alternative with their flexibility, affordability, and potential for innovation. Striking a balance between proprietary and open-source approaches enables businesses to harness the power of generative AI while optimizing costs and performance. By leveraging the lessons learned from the open-source software community, we can create a future where generative AI is accessible to all, driving unprecedented levels of innovation and customization.
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