From digital assistants to code generation, many small to medium businesses (SMBs) harbor lofty ambitions for generative AI (GenAI). The next step, however, is just as significant: whether to build their AI initiatives from scratch or simply secure a quick win with an existing AI tool.
For a resource-strapped business, this decision comes with a host of considerations, including AI readiness, existing infrastructure, and the amount of value derived, versus the effort required to realize their AI strategy. The stakes are greater for SMBs after all. With the prohibitive cost of computing power and other expenses needed to deploy AI models, many businesses not only lack the resources, they also often require help identifying and executing the best use cases.
Despite the buzz around GenAI, it’s crucial to determine the right AI model and infrastructure for your business early, instead of leveraging AI tools currently making headlines. This allows businesses to eliminate issues around under preparing or overprovisioning AI resources. To choose the right AI model, you should first examine how AI can add value to their operations while driving efficiencies. Then there’s data readiness and governance. With the increased risks that come with leveraging an emerging technology like GenAI, maintaining the quality, security, and privacy of data assets should be a top priority.
Customizing or training your AI models
As businesses embark on their GenAI journey, some examples of AI models they can deploy are:
- Pre-trained model: Trained on large data sets, pre-trained models allow users to pose questions and receive responses, such as ChatGPT. Since developers need not build the AI model from scratch, this approach will incur the lowest cost when compared with others, but the outcome is more for general use and not optimized for any specific industry or company. This can result in lower accuracy.
- Model augmentation: This allows companies to enhance their GenAI model by adding their own data. AI inference, in which AI models can make predictions or solve specific tasks, includes use cases like retrieval-augmented generation (RAG), which lets the AI model retrieve relevant information from vast pools of data such as user-specific data sets. As a result, responses are not only more accurate but are also context-specific.
- Fine-tuning model: By adjusting model weighting and incorporating proprietary data, fine-tuning models lets businesses get more out of their models with higher quality responses, with the models trained to address specific tasks for more accurate and specialized performance. This, however, requires more effort during setup compared with the previous models.
- Model training: This is about building a model from the ground up and training it with curated data sets, ensuring that outputs are at their most accurate. However, this entails a lengthy preparation process and requires an enormous amount of resources. It is usually reserved for solving highly complex problems.
Choosing the right mix of AI-optimized infrastructure
Various AI models demand new levels of costs, effort, and skills, and it doesn’t help that these are resources that SMBs often do not have enough of.
That is why choosing the right infrastructure underpinning AI investment remains a core challenge for many businesses. Investing in the most suitable infrastructure that supports such a deployment is dependent on a few factors: computational requirements, as well as model type, model size, and number of users. At the same time, having the right amount of storage capacity for data used during training and refining AI models is paramount.
Dell Technologies offers a range of solutions designed to help accelerate AI innovation in organizations of all sizes. Its AI PCs and Precision Workstations, for instance, are built to liberate IT teams so they can run pre-trained models and augment models without racking up significant costs. For SMBs looking to maximize performance across various applications, and expand compute and storage capacity, consider the PowerEdge rack servers for fine-tuning models and supporting billions of parameters.
Find out more about deploying your GenAI initiatives with Dell’s AI-ready infrastructure.
