When launching an AI initiative, organizations tend to focus on GPU compute or a foundational AI model. However, a network assessment should be the critical starting point.
AI workloads introduce unique data transfer patterns that can overwhelm legacy networks. It’s impossible to build, train or integrate an AI system successfully without knowing if the network infrastructure can carry the data.
Unassessed and unoptimized infrastructure is a primary driver of enterprise AI project failure. According to the Flexential State of AI Infrastructure Report, 96 percent of organizations experienced network-related performance issues directly impacting their AI workloads. Running a network assessment prevents stalled pilot programs and hidden operational cost spikes.
Standard business applications rely on North-South (user-to-server) traffic and need port edge speeds of 1Gbps to 10Gbps. For AI, the network must support simultaneous bursts of East-West traffic between compute nodes, with port edge speeds of 25Gbps to 100Gbps or InfiniBand.
Traditional apps can handle network delays of 50 milliseconds or more. AI requires ultra-low latency of less than 1 to 5 milliseconds. In high-throughput AI environments, even one delayed data flow can stall an entire workload.
Training an AI model requires syncing terabyte-scale datasets from localized storage hubs to cloud or on-premises compute engines. Basic quality of service (QoS) is adequate for standard business apps, but AI demands policy-based automation and microburst mitigation.
Deploying AI expands the data access footprint, as the models often process sensitive corporate and customer information. Effective network segmentation and access controls are critical to prevent training data from leaking into unauthorized zones.
A standard network assessment checks whether the network can carry normal business traffic without crashing. An AI-readiness network assessment evaluates whether the network can act asa high-speed data pipeline for high-performance computing clusters. It moves past general availability metrics to examine data center mechanics at the microsecond level.
A standard network assessment focuses on predictable, bursty traffic such as web browsing and video calls. It also has a North-South bias, evaluating data moving from centralized servers or the cloud to user endpoints. It assumes that applications can tolerate millisecond latency and some packet loss, relying on standard TCP retransmissions.
An AI-readiness network assessment recognizes the dominance of East-West traffic and optimizes for continuous, massive data flows between compute nodes. It measures microsecond latency with a focus on tail latency — the worst-performing 1 percent of data packets. It. It also assumes zero tolerance for dropped packets.
AI hardware and cloud AI compute tokens are incredibly expensive. Discovering that the network fabric cannot feed data to these systems fast enough after their purchased wastes capital. If the network fabric drops even 1 percent of data packets due to buffer overflows, costly GPUs must sit idle while waiting for the network to retransmit the lost data.
The assessment tells developers what the network is capable of, so they don’t design AI tools that the network cannot support. It also reviews segmentation and access controls to prevent the AI model from accessing or leaking sensitive training data.
Skipping this starting point can result in stalled pilots, in which an AI application works well in a test environment but grinds to a halt when it’s deployed to the wider corporate network. The technical gaps uncovered during the assessment also form the foundational line items for the AI project’s true budget.
Technologent’s Smart Connectivity practice conducts thorough network assessments to ensure that the network is prepared to handle AI workloads. Our assessments emphasize secure transport and secure access, along with secure hybrid cloud networking to support common AI architectures. If you’re planning an AI initiative, reach out to one of our specialists to schedule a consultation.