GenAI Architecture Series: Building Big with Small Models

LLMOPs Micro-Summit, San Francisco https://youtube.com/watch?v=KwcwlMhtFPk

As we navigate the landscape of artificial intelligence, a critical question emerges: how do we harness the power of Generative AI (GenAI) without succumbing to the high costs, complexity, and inefficiencies of massive models? This question is increasingly relevant not only for developers and data scientists but also for top executives and CTOs who are responsible for steering their organizations through the turbulent waters of technological innovation.

In a recent LLMOps Micro-Summit, Piero Molino , cofounder & CSO of Predibase , provided a compelling vision for the future of GenAI architecture. He explored how developers can leverage the latest innovations in large language model (LLM) technology to build powerful solutions with smaller, more efficient models. The discussion was particularly framed around the architecture employed by Apple in their newly launched Apple Intelligence platform and how similar approaches can be adopted using open-source tools and techniques.

This article distills key insights from Piero’s talk, focusing on how executives and CTOs can drive innovation in their organizations by adopting small model strategies that align with the latest trends in GenAI.

The Rise of Small Models: A Paradigm Shift

The AI industry has been dominated by the belief that bigger is always better. Massive models like GPT-4 have set the benchmark for AI performance, offering unmatched capabilities in various tasks, from language translation to creative writing. However, these models come with significant drawbacks: they are expensive to develop, deploy, and maintain; they are slow, which impacts user experience; and they are often too general to perform well in specialized tasks.

Piero highlighted a paradigm shift that is gaining momentum: the rise of small models. This shift is validated by industry leaders like Apple, which has pioneered a new approach in their Apple Intelligence platform. Instead of relying solely on massive models, Apple has integrated smaller, specialized models that are fine-tuned for specific tasks. These models run on-device, offering personalized experiences with lower latency and greater privacy.

Key Insight for Executives: Adopting small models isn’t just a technical choice; it’s a strategic move that can lead to significant cost savings, faster deployment, and enhanced control over AI applications. For CTOs, this means a shift towards more agile, cost-effective AI strategies that can be tailored to the specific needs of their organizations.

Apple’s GenAI Architecture: A Blueprint for the Future

Apple’s approach to GenAI is both innovative and practical. During their WWDC event, they unveiled the architecture behind their Apple Intelligence platform, showcasing how they achieve GenAI capabilities on-device. This architecture relies on a mix of on-device and cloud-based machine learning, with a strong emphasis on the former.

One of the key technologies that Apple has embraced is the use of adapters. These are small pieces of a model that are attached to a base model and fine-tuned for specific tasks. By doing so, Apple can deliver high-performance AI features on-device without the need for massive computational resources. These adapters enable tasks such as proofreading, summarization, tone adjustment, and more, directly on users’ devices: https://machinelearning.apple.com/research/introducing-apple-foundation-models

Key Insight for Executives: The use of adapters represents a new era of AI that is more sustainable and scalable. For organizations, this means that AI can be deployed at the edge—on devices or local servers—rather than relying entirely on cloud-based solutions. This approach can reduce costs, improve user experience, and address privacy concerns, making it a viable strategy for enterprises looking to scale their AI capabilities.

Leveraging Open-Source Tools to Build Like Apple

While Apple’s approach is impressive, not every organization has access to the same resources and proprietary technology. However, Piero demonstrated that similar results can be achieved using open-source tools and platforms like Predibase. He discussed how developers can fine-tune smaller, open-source models to achieve performance levels comparable to or even better than those of larger, proprietary models.

For instance, Piero shared insights from experiments where smaller models like LLaMA 3.1 were fine-tuned using open-source tools. The results were striking: these fine-tuned models not only matched but often surpassed the performance of larger models like GPT-4 on specific tasks, all while being significantly cheaper and faster to run.

Key Insight for Executives: Open-source tools offer a democratized approach to AI development, allowing organizations of all sizes to compete at the highest levels. By embracing open-source solutions, companies can reduce their dependency on expensive, closed-source models and gain greater flexibility in how they deploy and manage AI technologies. This is particularly important for CTOs who are tasked with balancing innovation with cost-efficiency.

Fine-Tuning: The Secret Sauce for High-Performance AI

A central theme of Piero’s talk was the importance of fine-tuning in achieving high-performance AI with small models. Fine-tuning allows developers to adapt a base model to a specific task by adjusting its parameters based on a smaller, task-specific dataset. This process can significantly enhance the model’s performance on that task while keeping the overall model size—and therefore its resource requirements—relatively low.

Piero explained that Apple’s use of fine-tuning through adapters is a key reason why they can deliver such high-quality AI features on-device. He also pointed out that similar fine-tuning techniques can be applied using open-source tools like Ludwig and LoRA (Low-Rank Adaptation), both of which are supported on the Predibase platform.

Key Insight for Executives: Fine-tuning is a powerful tool that can help organizations extract maximum value from AI models while minimizing costs. For CTOs, investing in platforms and tools that support efficient fine-tuning should be a priority. This will enable their teams to develop AI solutions that are not only high-performing but also cost-effective and scalable.

The Power of Dynamic Model Adaptation

Another critical innovation discussed by Piero is the concept of dynamic model adaptation. In traditional AI deployments, serving multiple fine-tuned models can be resource-intensive, as each model may require its own dedicated infrastructure. However, Apple—and by extension, Predibase—has developed a more efficient approach.

Piero described how dynamic loading of adapters can allow a single base model to serve multiple tasks efficiently. Adapters can be hot-swapped in and out of memory as needed, which means that organizations can run a wide variety of fine-tuned models on the same infrastructure without a significant increase in resource consumption.

Key Insight for Executives: Dynamic model adaptation represents a significant leap forward in AI efficiency. For organizations, this means that they can deploy multiple specialized AI models on a single infrastructure, reducing costs and improving scalability. CTOs should consider adopting platforms that support dynamic model adaptation to maximize the efficiency of their AI operations.

Real-World Impact: Case Studies and Practical Applications

Piero shared several case studies that illustrate the real-world impact of using small, fine-tuned models. In one example, a customer was able to reduce the cost of running AI models by 90% by switching from a large, proprietary model to a fine-tuned, open-source alternative. In another case, a company achieved a 250x reduction in model size while maintaining, and in some cases improving, performance.

These examples underscore the practical benefits of adopting a small model strategy. By fine-tuning and dynamically adapting models, organizations can achieve high levels of performance at a fraction of the cost and complexity of traditional approaches.

Key Insight for Executives: The shift to small models is not just a theoretical concept—it has tangible benefits that can drive significant cost savings and efficiency gains. CTOs and other decision-makers should evaluate their current AI strategies to identify opportunities where a small model approach could deliver similar results in their organizations.

The Role of Open Source in the Future of AI

A recurring theme in Piero’s talk was the growing importance of open source in the AI ecosystem. He argued that the pace of innovation in open-source models is now outpacing that of closed-source models. This is a significant development, as it means that organizations no longer need to rely on expensive, proprietary models to stay competitive.

Piero cited the example of the LLaMA 3.1 model, which has rapidly become a leading choice for developers due to its strong performance and flexibility. He also highlighted the increasing availability of fine-tuning tools and platforms that make it easier for organizations to customize and deploy open-source models at scale.

Key Insight for Executives: The rise of open-source AI models is a game-changer for the industry. By embracing open source, organizations can gain access to cutting-edge technology without the high costs and vendor lock-in associated with proprietary solutions. For CTOs, this means a greater ability to innovate and adapt in a rapidly changing technological landscape.

Building an AI Strategy for the Future

As AI continues to evolve, it is clear that the future belongs to those who can innovate with agility and efficiency. The insights shared by Piero Molino at the LLMOps Micro-Summit provide a roadmap for how organizations can achieve this by adopting a small model strategy, leveraging open-source tools, and focusing on fine-tuning and dynamic adaptation.

For top global executives and CTOs, the key takeaway is that building big doesn’t necessarily mean going large. By embracing the principles of small models, organizations can achieve powerful AI capabilities that are scalable, cost-effective, and tailored to their specific needs.

As one looks to the future of AI in one’s organization, consider the following strategic actions:

1. Evaluate Your AI Infrastructure: Assess whether your current AI infrastructure is optimized for the future. Consider whether a shift towards smaller, fine-tuned models could improve efficiency and reduce costs.
2. Invest in Open Source: Explore the open-source tools and platforms that are available to support your AI initiatives. Embracing open source can provide your organization with greater flexibility and access to the latest innovations.
3. Prioritize Fine-Tuning Capabilities: Ensure that your AI team has the tools and expertise needed to fine-tune models for specific tasks. Fine-tuning will be critical in achieving high performance without the overhead of massive models.
4. Adopt Dynamic Model Adaptation: Look for platforms that support dynamic model adaptation, allowing you to serve multiple AI models on the same infrastructure efficiently.
5. Stay Agile: The AI landscape is evolving rapidly, so it’s essential to remain agile. Be open to experimenting with new models, tools, and strategies to keep your organization at the forefront of AI innovation.