Dell Technologies

The rise of generative AI tools like ChatGPT has raised tantalizing prospects for workforce transformation. However, with widespread access to genAI tools has also come the increased potential for inadvertent exposure of company data. This leaves organizations walking a tightrope between democratizing access and increasing overall risk. How can companies empower workers with generative AI tools while still maintaining control?

As organizations weigh the pros and cons of various solutions, one answer has become increasingly clear: bringing generative AI solutions on premises offers more flexibility and more control over outcomes. This means organizations can pick the right models for their unique use cases, right-size them for their needs, augment or train them with their own data, and ensure the right security controls and guardrails are in place. In other words, generative AI within an organization’s direct control offers more options and more peace of mind.

What are the key advantages to running generative AI workloads on premises? Here are a few to consider:

You maintain greater control over security and data

First came widespread access to generative AI chatbots like ChatGPT in the workplace. Then came the stories of high-profile security leaks. This has put IT leaders in an all-too-familiar predicament: how to secure existing public tools or provide internal alternatives. For many organizations, the latter option is the only sensible solution -- they either cannot place certain data in the public cloud or cannot risk their sensitive or proprietary data even with additional security controls offered through enterprise generative AI solutions. But by adopting generative AI on their own hardware within their own environments, organizations maintain maximum control over their data and security posture. They can build solutions that work for them -- whether that’s retrieval augmented generation or fine tuning a generative AI model -- with the peace of mind that their data will not be inadvertently compromised or used to train public models.

You can create guardrails and minimize risk

When it comes to transparency and explainability, many public generative AIs have fallen under scrutiny. For example, organizations using common off-the-shelf models like GPT-4, probably have little idea what data it was trained on or how it generates its responses. This is where bringing a generative AI model in-house offers many advantages. Organizations can ensure models are trained with high-quality data, minimizing the chance of hallucinations or inaccuracies. They have the most control over the guardrails that govern sensitive or harmful outputs on premises and can potentially build safeguards that limit misuse. This puts organizations in control and helps them feel confident they can be protected from reputational risk.

You have greater control over costs

Because generative AI offers unprecedented capabilities in terms of content creation, data interrogation and code generation (among others) it offers tantalizing potential -- but not without cost implications. Many organizations have considered cloud-based proofs-of-concept for speed and cost considerations, but it turns out there are alternatives. There are equally fast ways to get running with generative AI on premises and many have found generative AI cloud bills are not all they’re cracked up to be.  

One of the advantages of running generative AI models on premises is that they give organizations the opportunity to right-size for their specific needs. This means more control over workload placement and being able to optimize for the infrastructure configuration that makes the most sense for their particular use case. Plus, in general, managing infrastructure on-premises offers more opportunities for cost efficiencies and the flexibility to adopt OpEx or CapEx models where desired. Or to put it more plainly, being in control of your infrastructure puts you in more control of your costs.

You have more control over energy consumption

Large language models -- the types of AI models that underpin tools like ChatGPT -- are called “large” for a reason. They’re made up of massive amounts of parameters. For example, GPT-4, the model which powers ChatGPT Pro, reportedly has 1.76 trillion parameters. This proliferation of parameters allows them to synthesize vast amounts of data in order to provide complex and expressive answers at lightning speed. It also means they’re compute-intensive, which requires energy.

Many organizations find, however, they don’t need the vast computational power of a massive large language model to power the use cases they’re aiming to address. In fact, there are many enterprise- or domain-specific use cases where training a smaller model on targeted data can be done on a smaller selection of hardware or even a high-powered workstation. This means, by bringing generative AI projects in house, they can right-size for their needs, which optimizes not only for cost but energy consumption as well. This means organizations get more efficient operations and can align with broader ESG commitments.

The age of AI transformation is here. Are you ready?

This signs are all around us: We're in the AI age. That’s why it’s more important than ever to think about how you’ll embrace it -- all the way down to the architecture that supports your business. Is it more beneficial to move large volumes of data closer to the AI, or might it be more advantageous and simpler to move the AI closer to your data? These considerations will influence how easily, cost-effectively and securely you can adopt generative AI within your organization.

Learn how to unlock faster outcomes with Dell Generative AI Solutions.

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