From digital assistants to code generation, many small to medium-sized businesses (SMBs) have lofty ambitions for generative AI (GenAI). However, the next step is just as important: whether they should build their AI initiatives from scratch or simply score a quick win with an existing AI tool.
For a resource-poor company, this decision brings a host of considerations, including AI readiness, existing infrastructure, and the amount of value derived, versus the effort required to deliver on their AI strategy. After all, the stakes are higher for SMEs. With the prohibitive cost of computing power and other costs required to deploy AI models, many companies not only lack the resources, but often need help identifying and executing the best use cases.
Despite the buzz surrounding GenAI, it’s critical to determine the right AI model and infrastructure for your business early, rather than leveraging AI tools that are currently making headlines. This allows companies to eliminate issues around under-preparation or over-provisioning of AI assets. To choose the right AI model, first investigate how AI can add value to their operations while increasing efficiency. Then there is data readiness and governance. With the increased risks associated with deploying an emerging technology like GenAI, maintaining the quality, security and privacy of data assets should be a top priority.
Adjust or train your AI models
As companies embark on their GenAI journey, some examples of AI models they can deploy include:
- Pre-trained model: Trained on large datasets, pre-trained models allow users to ask questions and receive answers, like ChatGPT. Because developers don’t have to build the AI model from scratch, this approach will have the lowest cost compared to others, but the result is more general-purpose and not optimized for a specific industry or company. This may result in lower accuracy.
- Model enlargement: This allows companies to improve their GenAI model by adding their own data. AI inference, where AI models can make predictions or solve specific tasks, includes use cases such as Retrieval-Augmented Generation (RAG), which allows the AI model to retrieve relevant information from large amounts of data, such as user-specific datasets. This makes the answers not only more accurate, but also context-specific.
- Refinement model: By adjusting model weighting and including proprietary data, model refinement allows companies to get more out of their models with higher quality responses, where the models are trained to tackle specific tasks for more accurate and specialized performance. However, this requires more effort to set up compared to previous models.
- Model training: This involves building a model from the ground up and training it with curated datasets to ensure the results are as accurate as possible. However, this entails a lengthy preparation process and requires a huge amount of resources. It is usually reserved for solving very complex problems.
Choosing the right mix of AI-optimized infrastructure
Different AI models require new levels of cost, effort and skills, and it doesn’t help that these are resources that SMBs often don’t have enough of.
That’s why choosing the right infrastructure to support AI investments remains a core challenge for many companies. Investing in the most suitable infrastructure to support such a deployment depends on a number of factors: computing requirements, but also 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 critical.
Dell Technologies offers a range of solutions designed to help accelerate AI innovation in organizations of all sizes. For example, the AI PCs and Precision Workstations are built to give IT teams the freedom to use pre-trained models and extend models without incurring significant costs. For SMBs looking to maximize the performance of various applications and expand computing and storage capacity, consider PowerEdge rack servers for refining models and supporting billions of parameters.
Learn more about deploying your GenAI initiatives with Dell’s AI-ready infrastructure.
