Navigate the Generative AI Inflection Point
Eighty percent of enterprises will use generative AI by 2026,1 and your organization—like many others—is likely racing to capture value and opportunity using this emerging technology. At the center of any AI initiative is the model itself. Enterprise organizations need to quickly and cost efficiently enable specific AI capabilities that are unique to their business.
Today, enterprise organizations rely on two primary methods for enabling customized generative AI capabilities. They can choose to fine-tune a general-purpose foundational model through further training. Or they can implement a technique known as retrieval-augmented generation (RAG) that facilitates customized outputs by connecting foundational models with specific datasets.
Retrieval-Augmented Generation vs. Model Fine-Tuning
RAG and fine-tuning both accelerate the journey to customized AI capabilities, but they do so in different ways.
In the fine-tuning method, organizations refine off-the-shelf models using their unique datasets. The foundational model provides a starting point, meaning your team doesn’t need the large amounts of time and data required to build from scratch. The processing demands of fine-tuning are less intense than training from scratch, so you likely won’t need heavy compute (such as a GPU cluster) to fine-tune your chosen foundational model.
On the other hand, RAG connects models with relevant data from your unique, proprietary databases to obtain and analyze organization-specific, up-to-the-minute information. This additional context informs the final output and, like fine-tuning, leads to the highly specific results that enterprise organizations need. Critically, the model is not fine-tuned or further trained in the RAG paradigm. Instead, it’s connected to the required knowledge bases through retrieval mechanisms.
Both approaches offer distinct advantages. Highly effective RAG-based implementations can be achieved with leaner hardware than fine-tuning. RAG also reduces the risk of hallucinations, can provide sources for its outputs to improve explainability, and offers security benefits since sensitive information can be kept safely in private databases.
It’s important to keep in mind that these approaches can also be used together. For more information on RAG, check out these guides:
- What Is RAG?: Learn how RAG works and explore the essential elements of a RAG implementation.
- How to Implement RAG: Get step-by-step guidance on how to put the RAG approach to use, including tips for knowledge base creation.
Explore Common Foundational Models
Both RAG and model fine-tuning rely on foundational models as central elements. While there are an ever-growing number of off-the-shelf foundational models available to your business, these six represent some of the most powerful and popular offerings in use today:
- Technology Innovation Institute (TII) Falcon LLM
- MosaicML MPT
- Hugging Face BLOOM
- Stability AI Stable Diffusion
- Databricks’ Dolly
- Meta AI Llama 3
- Mistral AI models
By basing your enterprise generative AI solution on these foundational models, you can significantly enhance time to value for your organization’s AI investment.
Of course, choosing a model is a complex process that depends heavily on your needs and business realities. Hands-on experimentation is one of the best ways to get familiar with these off-the-shelf offerings. All six of these models are available for your team to evaluate via the Intel® Tiber™ Developer Cloud.
Hardware Recommendations
In general, customizing an off-the-shelf model requires less computational power than training a model from the ground up. Depending on your needs, you may be able to run the required workloads via general-purpose hardware that your organization already owns. Or you may opt for specialized AI hardware to handle more-demanding workloads. In the case of RAG, you’ll likely choose between hardware types based on your throughput and latency requirements. Intel offers accelerated AI hardware for the entire range of customization needs:
- Large-scale, dedicated AI fine-tuning (< 1T parameters) and high-throughput, low-latency RAG: Intel® Gaudi® AI accelerators
- General purpose fine-tuning (< 10B parameters) and general RAG applications: Intel® Xeon® processors with built-in AI engines
When deploying fine-tuned models, the latest Intel® Xeon® processors and Intel® Gaudi® AI accelerators provide an optimized deployment platform that enables cost-effective inference.
You and your team can test performance across the entire AI pipeline on a range of hardware types via the Intel® Tiber™ Developer Cloud.
Software Tools
Across both customization approaches, software tools and development resources play an integral role in development and deployment. Without the right tools, you can face lengthy development times and headaches during deployment, especially when dealing with a heterogeneous mix of hardware.
To help solve these challenges, Intel offers an end-to-end development portfolio for AI. Our collection of resources and tools can help you build, scale, and deploy generative AI with optimized results.
For example, our optimized PyTorch library enables you to take advantage of the most-up-to-date Intel® software and hardware optimizations for PyTorch with just a few lines of code.
When pursuing customization through the RAG approach, integrated RAG frameworks such as LangChain, LLamaIndex, and IntelLab’s fastRAG can streamline and accelerate your efforts. RAG frameworks allow you to integrate AI toolchains across the pipeline and provide you with template-based solutions for real-world use cases.
Intel offers optimizations to help maximize overall pipeline performance on Intel® hardware. For example, fastRAG integrates Intel® Extension for PyTorch and Optimum Habana to optimize RAG applications on Intel® Xeon® processors and Intel® Gaudi® AI accelerators.
Meanwhile, OpenVINO™ toolkit plays an integral role in deployment. It’s an open source toolkit that accelerates AI inference with lower latency and higher throughput while maintaining accuracy, reducing model footprint, and optimizing hardware use. The toolkit streamlines AI development and integration of deep learning in generative AI—as well as computer vision and large language models.
For RAG applications, we provide several optimization libraries to help you maximize LLM inference on your hardware resources. Our Intel® oneAPI libraries provide low-level optimizations for popular AI frameworks, including PyTorch and TensorFlow, enabling you to use familiar open source tools that are optimized on Intel® hardware.
You can try the Intel® software resources highlighted in this article—and many others—via the Intel® Tiber™ Developer Cloud.
You can also consult our generative AI development page for a curated collection of enablement resources for your generative AI projects.
Chart a Simpler Course to Enterprise AI
As you progress your generative AI initiative from model customization and proof of concept to deployment, you can optimize efficiency and accelerate innovation with tools and technologies from Intel and our ecosystem of global partners.
By choosing Intel for your AI platform, you can maximize the value of the infrastructure you already have while ensuring the openness and interoperability you’ll need to sustain success in the future. Our investments in reliability and manageability help deliver smoother, simpler AI operations across the pipeline. Our open platforms and high-performance, low-TCO hardware allow for the flexible, efficient deployments you need to enable generative AI at scale.
As part of the Linux Foundation Open Platform for Enterprise AI, we’re working to develop an ecosystem orchestration framework that efficiently integrates generative AI technologies and workflows, enables quicker adoption, and enhances business value through collaborative development. Our current contributions include a set of generative AI architectures that can help expedite your initiative:
- A chatbot on Intel® Xeon Scalable processors and Intel® Gaudi® AI Accelerators
- Document summarization using Intel® Gaudi® AI Accelerators
- Visual Question Answering (VQA) on Intel® Gaudi® AI Accelerators
- A copilot designed for code generation in Visual Studio Code on Intel® Gaudi® AI Accelerators
Start Customizing Generative AI Models on Intel Today
Generative AI is primed to bring major disruption to enterprise organizations across virtually all industries—from manufacturing to healthcare, retail, and beyond.
As you seek to enable the unique AI capabilities required for your organization and AI applications, fine-tuning and RAG provide great pathways to faster time to market and ROI. Using today’s leading foundational models in combination with the purpose-built Intel® AI portfolio, you can simplify, streamline, and accelerate generative AI success for your organization.
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