by Steve Rumsby, VIAVI AI workloads, particularly large‑scale training and distributed inference, place unique demands on data center fabrics. While traditional network test metrics such as throughput, average latency, and packet loss are still necessary, they are not sufficient to characterize real AI performance. A key reason for this is the straggler effect, where the synchronous nature of AI training means that P99 or P99.9 outliers can have an undue effect on the east-west sync and burst traffic. As a result, a delayed packet or slow communication flow can stall synchronization across thousands of GPUs, extending completion time for the entire workload. Traditional metrics struggle to quantify this effect, and job completion time (JCT) has emerged as a key KPI for AI systems. Indeed, it is now seen by many as the most meaningful performance metric for AI fabrics , ahead of even raw link‑level metrics. In this article, we’ll look at why this is, and the implications of switching to methodologies that use it. Why traditional network KPIs fail for AI Before AI, enterprise networks tended to be transactional, with request/response workloads. KPIs therefore focused on ways to monitor the network’s effectiveness for these workloads, with metrics such as peak throughput, average latency, jitter, and packet loss becoming the de facto measurements taken. However, in AI, these KPIs (while still valuable) by themselves are ill-suited to predicting AI job performance. The traffic of AI workloads is hugely different, generating highly synchronized and bursty flows, with east-west data transfers happening in a many-to-many pattern. This creates significant stress for buffers, congestion control, and collective communication mechanisms that these traditional KPIs simply do not highlight. Numerous industry studies have shown that these metrics often fail to expose tail-latency, congestion, and synchronization effects that materially impact AI training performance. Job completion time A better view can be obtained by tracking the total elapsed time from the AI job being admitted to the system through to its successful completion with the final output or checkpoint. This is Job Completion Time, or JCT, and while it cannot be described as a pure compute metric, in large-scale AI training it will take into consideration and measure a range of essential factors. These include data staging and ingestion, distributed compute execution, the efficiency of collective communication operations (such as all-reduce vs. all-gather), synchronization barriers, and checkpointing and storage interactions. In large-scale distributed training environments, JCT is often heavily influenced by network behavior because communication phases must complete before computation can continue. The reason for this is that AI workloads are extremely sensitive to factors such as tail latency, incast congestion, microbursts, buffer pressure, and load imbalance across paths. When measured only using traditional isolated link metric approaches, all of the above will have merely negligible effects, as these look at the average latency or throughput. But the effect on the network will be huge as they force costly GPUs to idle while waiting for straggling communications. By measuring the complete start-to-finish time, we instead get to see the full picture, with JCT shown to be a direct correlative indicator of several vital outcomes, including time-to-model, time-to-insight, cost per training run, and cluster usage efficiency. Figure 1. AI fabric performance can be understood as a layered KPI framework in which traditional network metrics and diagnostic indicators ultimately feed into Job Completion Time (JCT), which directly correlates with business outcomes. (Image: VIAVI) As such, modern industry test frameworks explicitly identify JCT, alongside throughput and latency, for use in validating AI data center fabrics. Implications of using JCT as a network KPI Before we examine