Partner POV | Tomorrow's AI Applications Need a Better Way to Network

This article was written and contributed by our partner, Graphiant.

It's been two years since the release of ChatGPT, and the AI hype shows no signs of slowing. Business and government organizations are now investing heavily in building private AI capabilities.

Organizations want their own large language model (LLM) that does everything other AIs can do but in a way the organization maintains ownership and control. It is the next major phase of AI growth that is actively in flight that comes with its own challenges.

Business and government datasets are enormous and geographically distributed. Whether training AI models or running local workloads, backhauling all data to a centralized server farm increases inefficiency.

As AI adoption grows, access to energy will become a major limiting factor. The graphics processing units (GPUs) in AI clusters consume huge amounts of power, and even the largest hyperscale data centers have a finite amount.

These factors point to a single conclusion: the future of AI will be distributed.

The networks connecting all that distributed compute become critically important. The current wide-area network (WAN) infrastructures aren't up to the task. Fortunately, there's a model perfectly suited for distributed private AI: network-as-a-service (NaaS). As AI evolves, the advantages of flexible, on-demand private networks will only grow.

Inside private AI

The logic of private AI is inescapable. When you have your own AI server farm, you can train your models, run your workloads, and build whatever specialized intelligence you choose to benefit your assets and business. You can productize that intelligence without worrying about the risks of exposing your organization's private and extremely valuable datasets to a third party.

Organizations worldwide are already pursuing this model. One recent forecast predicts the global GPU market will exceed $65.2 billion in 2024 and reach $274.2 billion by 2029 - a 33% growth rate. Given the distributed nature of most private datasets, and the need to spread out the power and space requirements of AI clusters, a distributed architecture is the only viable solution.

Distributed private AI introduces novel networking challenges that organizations will struggle to address with traditional WANs. These include:

• High costs and complexity: Most organizations are hesitant to build their own distributed AI network. The capital expenditure for equipment is enormous, as are the operational costs of deploying and maintaining that infrastructure. Traditional WANs also use tunnels that must be manually updated whenever something changes. And for businesses looking to exploit private AI to gain a competitive edge, the long timelines needed to build new networks may be unacceptable.
• Demanding performance requirements: Many AI applications have capacity and latency requirements that demand path control and optimization for AI workloads. Yet large distributed data networks encounter occasional problems that disrupt connectivity or degrade performance. You don't want to have to identify optimal paths yourself.
• Limited software options: Organizations building their own private AI networks are constrained by the available data networking software. Little of what's out there was designed with AI in mind. Does it make sense to develop the necessary software stack yourself, on top of infrastructure costs?
• Security concerns: There's always a risk of malicious actors sniffing data in transit, but with AI, the amount of data those attackers could access is truly vast. With exploding demand for quality training data, those private datasets have become extremely valuable. If someone siphons off your data to train their own LLM, that's a major loss. Organizations need end-to-end visibility to guard against leakage and ensure that no outside party can ever access the data.

A smarter solution for distributed AI

It would be simpler if private AI networks worked like cloud resources. If connections just "happened," with all the required data assurance and without sacrificing privacy, data sovereignty, or regulatory compliance. Welcome to network as-a-service.

NaaS provides a private network service to interconnect all an organization's distributed compute stacks. And like other cloud resources, you don't have to worry about which server, in which data center you need to connect. All server farms get linked together as a prebuilt, programmable network that you can use on a committed throughput basis.

NaaS is purpose-built for distributed private AI. It provides the flexibility to move data wherever it's needed - cloud-to-cloud, cloud-to-non-cloud, cloud-to-edge, in any direction - with holistic visibility, security, and path and policy control. NaaS provides:

• Simplicity and speed: Organizations can connect distributed compute and datasets anywhere, without having to architect physical infrastructure, provision tunnels, or manage ongoing maintenance. NaaS lets organizations implement private AI networks in a fraction of the time it takes to build one themselves.
• Data assurance: Modern NaaS solutions maintain end-to-end encryption, assuring that private data is never exposed outside your domain. This is essential as private AI grows. Given the size and value of AI datasets, any service that decrypts traffic in transit is a prime target for attack.
• Improved power efficiency and costs: As you transport larger AI workloads, you don't want to worry about static pre-existing networks forcing them down more expensive or poorer-performing paths. Modern NaaS solutions dynamically determine the optimal route for each workload.

Looking ahead

The biggest advantage of NaaS in private AI is the agility it provides for navigating this incredibly fast-moving space. We are still in very early days. As AI adoption grows, practically every organization will bump up against the same limitations - continually expanding the world's distributed AI footprint. That means more GPUs, more regional data centers, more new tools and applications and datasets hosted in many more locations.

For a technology evolving so rapidly, with so much ongoing experimentation, sinking significant capital into a fixed data network is a risky bet. Your mission-critical private AI network should be a service, so that you can change it whenever you need to.