Two years have passed since the release of ChatGPT, but the AI hype shows no signs of slowing down. Companies and government agencies are now investing heavily in building AI. private AI functions.
Organizations want their own Large Scale Language Models (LLMs) that can do everything other AI can do, but over which the organization retains ownership and control. This is the next major stage in the ongoing growth of AI, which comes with its own challenges.
Enterprise and government datasets are large and geographically distributed, and backhauling all that data to centralized server farms, whether for training AI models or running local workloads, creates multiplying inefficiencies.
As AI adoption expands, access to energy becomes a major limiting factor: Graphics Processing Units (GPUs) in AI clusters consume vast amounts of power, which is limited even in the largest hyperscale data centers.
These factors point to one conclusion: the future of AI is decentralized.
The network that connects all that distributed computing will be crucial. Today's wide area network (WAN) infrastructure is not up to the task. Fortunately, there is a perfect model for distributed private AI: Network as a Service (NaaS). As AI evolves, the benefits of a flexible, on-demand private network will only grow.
Inside Civilian AI
The logic of private AI is inescapable: if you have your own AI server farm, your Model, execution your Analyze your workloads and build specialized intelligence that benefits your assets and your business. Productize that intelligence without worrying about risking exposing your organization's private and highly valuable data sets to third parties.
Organizations around the world are already pursuing this model: Recent forecasts predict 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 distribute the power and space requirements of AI clusters, a distributed architecture is the only viable solution.
Distributed private AI brings new network challenges that organizations find difficult to address with traditional WANs, including:
- Costly and complex: Most organizations are hesitant to build their own distributed AI networks. The capital expenditures for equipment are significant, as are the operational costs of deploying and maintaining that infrastructure. Traditional WANs also use tunnels that must be manually updated every time something changes. And the long time frame required to build a new network can be unacceptable for businesses hoping to leverage private AI to gain a competitive advantage.
- Stringent performance requirements: Many AI applications have capacity and latency requirements that require path control and optimization for AI workloads, but large-scale distributed data networks sometimes experience issues that result in dropped connections or poor performance, and you don't want to have to figure out the optimal path yourself.
- Restricted Software Options: Organizations building their own private AI networks are constrained by the data networking software available; very little was designed with AI in mind. On top of the infrastructure costs, does it make sense to develop the required software stack yourself?
- Security concerns: There is always a risk that a bad actor could eavesdrop on data in transit, but with AI, the amount of data an attacker can access is huge. With the demand for quality training data exploding, these private datasets become extremely valuable. If someone steals the data to train their own LLM, it's a huge loss. Organizations need end-to-end visibility to prevent leaks and ensure that external parties can't access the data.
A Smarter Solution for Decentralized AI
It would be much easier if private AI networks acted like cloud resources. Connections just “happened” with all the necessary data guarantees in place, and without sacrificing privacy, data sovereignty, or regulatory compliance. Welcome to Network-as-a-Service.
NaaS provides a private network service to interconnect all of an organization's distributed computing stack. Like any other cloud resource, you don't need to worry about which server in which data center you need to connect to. All server farms are linked together as a pre-built programmable network that can be used on a committed throughput basis.
NaaS is purpose-built for distributed private AI, with comprehensive visibility, security, path and policy control, and the flexibility to move data wherever it needs to go in any direction: cloud-to-cloud, cloud to non-cloud, cloud to edge. NaaS offers:
- Simplicity and speed: Organizations can connect distributed computing and datasets from anywhere without having to design physical infrastructure, provision tunnels, or manage ongoing maintenance. NaaS allows organizations to implement private AI networks in a fraction of the time it would take to build their own.
- Data Assurance: Modern NaaS solutions maintain end-to-end encryption to ensure private data is not exposed outside of the domain, which becomes important as private AI expands: Given the size and value of AI datasets, services that decrypt traffic in transit become a prime target for attack.
- Improved power efficiency and cost: When transporting large AI workloads, you don't need to worry about existing static networks forcing you to choose more costly or less performing paths: modern NaaS solutions dynamically determine the best route for each workload.
Looking to the future
The greatest benefit of NaaS in private AI is the agility it gives you to navigate this incredibly fast-changing space. We're still in the early stages. As AI adoption grows, virtually every organization will run into the same limitations: a continually expanding distributed AI footprint around the world. That means more GPUs, more regional data centers, more new tools and applications, and datasets hosted in more locations.
For a technology that is evolving so rapidly and with a lot of ongoing experimentation, investing significant capital in a fixed data network is a risky bet. A mission-critical private AI network should be a service, able to be modified at any time if needed.
