Presented by Intel
The remarkable natural language capabilities of generative AI, powered by large language models (LLMs), have firmly established AI in the public spotlight. These extensive models may represent one of the most significant advancements of our time. Paradoxically, the evolution of AI now sees numerous open-source LLMs leading to the development of thousands of domain-specific LLMs for enterprises.
LLM-based AI services can automate routine tasks and serve as productivity assistants. However, for AI to tackle complex challenges, enhance an organization's core mission, and personalize consumer experiences, LLMs must become specialized. Many industry experts agree that the bulk of artificial intelligence in most organizations will be delivered by agile expert models operating on existing enterprise IT, edge, and client infrastructure.
Do LLMs Provide a Competitive Advantage?
LLMs with hundreds of billions of parameters are trained on web-scale data using data center-scale clusters, resulting in versatile AI platforms for general inquiries deployed by cloud providers or AI services companies. The development of such models costs hundreds of millions of dollars, with ongoing operational expenses in the tens of millions. These large models excel in generating generalist, non-proprietary results from publicly available data. As most organizations utilize similar generative AI services through API calls, the primary advantage lies in merely keeping pace with the competition.
To create unique products and services, improve customer engagement, and boost cost efficiency, organizations need accurate and timely models trained on domain-specific private data. This prevents errors, bias, and potential damage to reputation. The complexity of the use case directly correlates to the precision of the model, emphasizing the importance of incorporating proprietary data. Large models can be cumbersome and inefficient for mission-critical enterprise applications, making smaller, more agile models a preferable choice.
Fortunately, open-source, pretrained small LLMs exist that are 10x to 100x smaller than their larger counterparts but maintain high accuracy.
These smaller models can be fine-tuned quickly using Retrieval-Augmented Generation (RAG) methods with private data, creating reliable expert models tailored for specific business needs. Organizations can now develop a model over lunch and deploy it on existing servers, avoiding the lengthy and costly processes associated with large models. This approach is sustainable and economical for scaling AI across applications.
Large Foundation AI Models and Services:
- Advantages: Incredible versatility, compelling results, fast integration via APIs, web-scale datasets.
- Disadvantages: Complex management, expensive to train and maintain, potential for hallucination and bias, security concerns, unknown data sources.
Small Language Model Ecosystem:
- Advantages: Smaller size with improved accuracy, enhanced data privacy and security, explainability, economical fine-tuning and deployment.
- Disadvantages: Requires few-shot fine-tuning, necessitates indexation of source data, reduced range of tasks.
Why Enterprises Will Manage Their Own LLMs
Most organizations will leverage API services for routine tasks while adopting private AI models for business-specific cases. When deciding which AI models to self-manage, consider:
- Data Privacy: Safeguard sensitive information and gain a competitive edge while complying with data governance regulations.
- Accuracy: Ensure reliable operation of mission-critical applications to protect reputation.
- Explainability: Be able to trace results back to data sources before making significant decisions and continuously monitor for consistency.
- Cost: Self-operating persistent models on existing IT infrastructure is typically less expensive.
- Proximity: Co-locating models with applications ensures swift human response times.
- Integration: Seamless deployment within existing business logic and IT decision-making systems.
Understanding Your Requirements and Model Options
AI is frequently misrepresented as isolated applications competing for performance. However, we believe that AI will eventually become an integral function in every application, utilizing existing IT infrastructure. Understanding your data, use case requirements, and AI model options is essential for successful implementation. While some enterprises with substantial data and unique business tasks may wish to develop their own large language models, most will benefit from agile open-source models for tasks like customer service or order processing.
AI proliferation necessitates accelerated computing in accordance with application demands. Models will be acquired from the open-source ecosystem, fine-tuned with private data, or integrated with commercial software. The groundwork for a production-ready AI use case involves extensive work beyond the LLM itself, encompassing data ingestion, storage, processing, inference serving, validation, and monitoring. Thus, a computing platform must support data preparation, model building, and deployment.
Intel offers an end-to-end AI platform, including the Intel® Gaudi® accelerator for optimal cost-effective performance—reportedly delivering 4x the performance per dollar of Nvidia's H100—and the 5th Gen Intel® Xeon® general-purpose CPU with built-in AI features, catering to small LLMs and other AI workloads.
- Models: Automated model recipes and optimization for thousands of models on platforms like Hugging Face, GitHub, and the Gaudi Developer Hub.
- Software: Intel® Gaudi® Software and Intel AI software suite validated with over 400 AI models across industry-standard frameworks.
- Enterprise-Ready: Fine-tuned AI models validated for production using VMware Private AI and Red Hat OpenShift on Xeon-based OEM servers.
Your Generative AI Journey Begins Now
The journey for enterprises starts with identifying business use cases—whether it's cost savings through streamlined operations, increased revenue via enhanced customer experiences, or the removal of mundane tasks for improved employee satisfaction. Developers should begin with an open-source LLM or use case-specific model, ensuring they understand data requirements and have the right software tools for optimal cost performance and ease of use.
Explore Intel's open-source generative AI models on Hugging Face and embark on your generative AI journey. For more insights, consider signing up for a free trial on Intel Developer Cloud or learning about generative AI in our monthly GenAI webinar series.
Contributors:
- Jordan Plawner, Senior Director, Global AI Product at Intel
- Susan Lansing, Senior Director, Gaudi Accelerator at Intel
- Sancha Norris, AI Product Marketing Strategist at Intel
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