With the rapid evolution in AI, business and government organizations must invest heavily in building private AI capabilities. Organizations must train their own large language model (LLM) that does everything AI can do, while also maintaining ownership and control. It is the next major phase of AI growth that is actively in flight that comes with its own challenges, especially in network communications and data transfers for these AI workloads.
Business and government datasets are enormous and geographically distributed. Whether training AI models or running local workloads, backhauling all data to only centralized compute locations increases inefficiency. As AI adoption accelerates, access to energy will become the major limiting factor. The graphics processing units (GPUs) for AI consume huge amounts of power and cooling, and even the largest hyperscale data centers will not be able to provide the power and cooling required for the workloads that must be supported.
These factors point to a single conclusion: the future of AI will be distributed.
The networks connecting all the distributed compute are critically important. The current wide-area network (WAN) infrastructures are designed from an older time for older applications. As AI evolves, the advantages of flexible, on-demand private networks will grow.
Inside private AI
Private AI is critical for business expansion. With private AI, you can train your models, run your workloads, and build specialized intelligence tailored to your assets and business. You productize that intelligence without worrying about the risks of exposing your organization’s private and valuable datasets to a third party. Organizations worldwide are pursuing this model. One forecast predicts global GPU market will reach $274.2 billion by 2029 — a 33% growth rate over 5 years. Given the distributed nature of private datasets, and the need to spread out the power and space requirements of AI clusters, a distributed architecture for connectivity and network transformation is the only viable solution.
Distributed private AI brings novel networking challenges that organizations will struggle with using older technologies. These include:
High costs and complexity: Organizations hesitate to build their own network. The capital operation costs of deploying and maintaining that infrastructure are very large. Traditional networks also use fixed lines connectivity with rigid networking and tunnels that must be manually updated for changes. And for businesses looking to use private AI to gain a competitive edge, the long timelines needed to build new networks are unacceptable.
Demanding performance requirements: 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.
Limited software options: Organizations building private AI are constrained by the available data networking software. Little of what’s out there was designed with AI in mind.
Security concerns: There is always risk of malicious actors sniffing data in transit, but with AI the amount of data those attackers could access is vast. With exploding demand for quality training data, those private datasets are extremely valuable. Organizations need end-to-end assurance and guarantees along with visibility to guard against leakage and ensure that no outside party ever accesses the data.
A smarter solution for distributed AI
A new architecture for distributed AI Edge Networking that is both environmentally sustainable for reduced power consumption with all the required data assurance, and doesn’t sacrifice privacy, data sovereignty, or regulatory compliance is the Graphiant architecture.
Graphiant provides a private network to interconnect an organization’s distributed compute. All compute is linked together as a prebuilt, programmable network that uses a committed throughput. It is purpose-built for distributed private AI. It provides flexibility to move data — cloud-to-cloud, cloud-to-non-cloud, cloud-to-edge, in any direction — with visibility, security, and path and policy control.
Graphiant provides:
Simplicity and speed: Organizations connect distributed compute and datasets. Graphiant lets organizations implement private AI networks in a fraction of the time it takes to build one.
Data assurance: Graphiant maintains end-to-end encryption, assuring that private data is never exposed outside the 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 Graphiant transports larger AI workloads, it protects the data by removing static pre-existing networks and avoiding more expensive or poorer-performing paths, delivering dynamically deterministic optimal pathing for each workload.
Looking ahead
The biggest advantage for private AI provided by Graphiant is the agility it provides for navigating this incredibly fast-moving space. As AI adoption grows, every organization will experience the same limitations - continually expanding the world’s distributed AI footprint. That means more GPUs, more regional data centers, more tools and applications and datasets hosted in many more locations.
For a technology evolving so rapidly, with so much ongoing experimentation, capital into the network should be delivered as a mission-critical private AI network service.