In a recent survey report conducted by Domino Data Lab and Wakefield, 98 percent of CDOs and CDAOs said that the companies bringing AI and machine learning solutions to market fastest will survive and thrive. Nearly all participants in the same study (95%) also say that operationalizing AI will enable these companies to achieve a revenue boost.

We share this near-universal belief that AI has the potential to determine tomorrow’s winners and losers. As we work with enterprises in every sector scrambling to figure out their ideal formula for AI transformation, one thing is clear: the “ideal” is unique to each company.

The technology ecosystem supporting AI, large language models (LLMs), and machine learning is evolving at unprecedented speed. Amid all that change, enterprises need to be confident that their AI investments will remain relevant. It doesn’t feel like hyperbole to say that by the time one finishes researching all the components available for purchase to assemble the ideal AI stack, they’d have to start over again because the technology would be outdated. 

On-Premises Infrastructure Limits AI Flexibility and Business Agility

Flexibility in your AI operations leads to the business agility your organization needs to outcompete rivals. Flexibility is essential, because what is considered the “ideal” AI stack has the propensity to change as quickly as it takes a tech company to write a press release announcing their latest product innovation. Need proof? Just look at the NVIDIA GPU lineup. The new DGX GH200, when clustered in a group of 250, can perform as a single GPU and provide 1 exaflop of performance and 144 terabytes of shared memory. This is nearly 500x more memory than the previous generation NVIDIA DGX A100, which was introduced as recently as 2020.

The incredible pace of innovation that NVIDIA and others are bringing to the GPU marketplace is unlocking unprecedented potential for AI that even only recently felt far off in the future. Harnessing the unparalleled power of the GH200 will be transformative for many, and yet, soon enough the GH200 will be overtaken by a new chipset that delivers even greater performance. 

If you’re considering investing in GPUs for your on-premises data center, you should first explore the tradeoffs between the advantages of on-prem (avoiding the steep prices inflicted on customers by the hyperscalers) and the drawbacks of freezing your GPU capacity at one moment in time. There will likely be a short window of cutting-edge technical superiority, but you’ll be limiting your AI initiatives geographically to the region where these shiny new GPUs are deployed. Worst still, this investment could lead to negative ROI while also hobbling your AI initiatives with sunk costs in technology that can’t keep pace with your competitors. Instead, enterprises should take an agile approach by choosing more flexible alternatives that don’t require a steep capital outlay and promise better results.

Composability Offers Flexibility, Agility and More 

Unless your company has unlimited resources, you can’t get the flexibility and agility you need by buying, deploying, configuring, supporting, and upgrading the GPUs in your own data center. On-prem deployments place limits on distributed enterprises looking to scale data science and AI across their business operations in multiple geos and keep ahead with technology innovation. For flexibility and agility in managing your AI and machine learning initiatives, you must adopt a multicloud strategy that allows you to assemble the optimal tech stack comprising state-of-the-art composable infrastructure and components.

Let’s break that concept down into its two essential components: composability and multicloud

First, why composability, and what is it? In the abstract, composability is about assembling completely independent components into a functional whole that is infinitely changeable. A composable AI stack comprises technology components that by definition have no interdependencies and can, therefore, be swapped out as needed to keep pace with evolving technologies and address the changing requirements of your AI initiatives. Composability makes change easier, faster, safer, and less costly – benefits that are impossible to obtain if you’ve concentrated your GPU investment in an on-prem deployment.  

That leads to the second component: multicloud. Rather than locking up all your GPU eggs in one on-premises basket, renting cloud GPUs to power some or all of your AI and ML initiatives allows you to offload the responsibilities for procuring, deploying, configuring, and maintaining GPU infrastructure to one or more cloud providers that specialize in GPU operations. This is no small advantage, as the time and expense involved in configuring a GPU stack are considerable even for GPU-experienced administrators. Further, renting cloud GPUs gives you instant access to GPU capacity wherever your cloud suppliers have data center locations, enabling your organization to engage in global AI operations. 

Composable Cloud Is the Answer to Creating and Maintaining the Ideal AI Stack

Composable cloud is the powerful combination that ensures enterprises can build just the right AI stack for their current business requirements and reconfigure that stack as needed by swapping out components when conditions change. A composable cloud comprises more than just infrastructure as a service (IaaS). It also includes the platform (PaaS) and application (SaaS) layers of the AI stack to equip data science teams with all the tools they need to build, deploy, scale, and optimize cloud-based AI applications.

There are four tenets that govern the composable cloud: 

  1. Every component must be modular; there can be no monoliths. Composable cloud requires a discrete set of microservices, and each microservice can be packaged as its own container that can be independently deployed and scaled in the runtime environment. 
  2. Every component must be atomic. With a nod to atomicity in chemistry, atomic components in composable cloud form the basic building blocks or the smallest unit of value that can be encapsulated to deliver a discrete outcome when called. 
  3. There are no dependencies among components. If a customer chooses to use a particular tool or service, they can use that component independent of any other component they may or may not choose to use. 
  4. All components must be individually and collectively orchestrateable. When a customer chooses to mobilize any number of containers, all of the containers must organically work together. If component one needs data from component two, it can find component two, call component two, and get an output from component two. Everything works together. Further, every component has to be fully autonomous and discoverable, and each must expose what it is, what it does, and what inputs it needs. 

To enjoy these advantages you must work with providers that embrace composability. The not-for-profit industry group MACH Alliance certifies vendors that provide composable best-of-breed cloud offerings for the full cloud stack, including IaaS, PaaS, and SaaS layers, which yields the following benefits:

  • Flexibility: Composability enables customers to easily and quickly change the components of their cloud stack to adapt to new opportunities.
  • Choice: Composability enables customers to select from a variety of vendors to assemble their ideal cloud stack, and change again as needed.
  • Scalability: Composability enables customers to rapidly rightsize their cloud stack to their current conditions and needs. 
  • Affordability: Composability enables customers to pay for only the services they use.

A composable cloud approach enables customers to pick and choose among components and microservices at all layers of the cloud stack without making long-term commitments or being forced to pay for services they don’t use or need. Choosing components and microservices offered exclusively by MACH-certified vendors also offers the side benefit of reducing the time infrastructure teams need to invest in researching the components of their AI stacks to be sure all components will be interoperable.  

Let Composability Be Your Guide to Assembling the Ideal AI Stack

The winners in the race to operationalize AI will be the companies that can trace their success to the composable cloud and its benefits. Unfortunately, not all cloud and service providers embrace composability. Let composability be your primary criterion for choosing the vendors you will work with to power your AI operations. Flexibility, choice, scalability and affordability will follow. So, too, will business agility when you assemble a composable AI stack that will, by definition, perpetually remain ideal.

About the Author

Kevin Cochrane, Chief Marketing Officer, Vultr. Kevin is a 25+ year pioneer of the digital experience space. Now at Vultr, Kevin is now working to build Vultr’s global brand presence as a leader in the independent Cloud platform market.

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