![\"\"](\"http:\/\/vdcc.vn\/wp-content\/uploads\/2026\/06\/Data-Workload.webp\")
<\/p>\nAI workload volatility forces data centers to run secondary tasks, inflating energy use, infrastructure demands, costs, and grid pressure.<\/p>\n
<\/p>\n
Scrutiny over the power consumption of AI data centers has reached a boiling point. In response, policymakers, utilities, and tech companies are trying to figure out how to build enough power generation to meet demand, without raising electricity costs for everyday consumers. Yet, one critical question has received surprisingly little consideration: why do AI data centers need so much power in the first place?<\/p>\n
A significant but overlooked part of the answer lies not in the models themselves, but in how data centers manage rapid fluctuations in power demand created by modern AI workloads. Now under intense pressure to rein in electricity usage, the industry can no longer afford to rely on conventional methods such as secondary workloads to smooth out power demand.<\/p>\n
The root of the problem lies in the way large AI models are trained. Modern training is often bulk-synchronous: thousands of GPUs perform computation in parallel, then pause briefly to exchange data and synchronize results across the cluster. During these pauses, many GPUs sit idle before ramping back up again.<\/p>\n
At hyperscale, these synchronized idle periods create sharp, rapid drops in power demand across the entire data center. Such fluctuations can stress transformers, power distribution units, and even upstream grid components, risking outages, or costly downtime.<\/p>\n
One of the most common ways data center operators manage these fluctuations is by running secondary workloads whenever GPUs would otherwise be idle. These workloads are not part of the primary AI training job; instead, they exist to prevent power demand from dropping too sharply when GPUs are idle. They run just long enough to fill the brief dips in power usage, then yield instantly when the primary computation resumes. At Oracle, for example, this process is guided by a millisecond-scale \u201cGPU heartbeat,\u201d which continuously measures GPU activity and triggers secondary workloads with near-instant timing.<\/p>\n
This approach stabilizes the data center\u2019s power demand profile but unnecessarily inflates overall power consumption and introduces other inefficiencies that compound rapidly at scale.<\/p>\n
Secondary workloads generally fall into two categories: productive workloads that perform useful work during idle periods and dummy workloads that exist solely to maintain a stable power profile. Both come with significant trade-offs.<\/p>\n
In some cases, operators use productive secondary workloads — tasks that need to run eventually and can take advantage of idle GPU cycles. However, productive secondary workloads compete with the primary AI training job for GPU resources, memory bandwidth, and thermal headroom. The result is lower effective performance for the main workload because training takes longer, synchronization overhead increases, or throughput is reduced.<\/p>\n
In other words, productive secondary workloads stabilize power demand by sacrificing performance. At the scale of large AI clusters, even small efficiency losses translate into significant increases in training time, cost, and time\u2011to\u2011market.<\/p>\n
When performance cannot be compromised, operators turn to dummy workloads, which perform meaningless calculations. Dummy workloads do not interfere with training performance but also do not produce any useful output. In large data centers with tens of thousands of GPUs, this represents a massive and largely invisible source of energy waste.<\/p>\n
While running secondary workloads is often framed as a minor power-management tactic, they create cascading operational consequences that extend far beyond wasted electricity or performance losses.<\/p>\n
If the industry is serious about reducing the power footprint of AI data centers, it must move beyond workarounds such as secondary workloads and adopt smarter ways to manage rapid demand fluctuations. Using extra computation \u2013 whether productive or dummy \u2013 to flatten the power curve is ultimately a costly substitute for better system design. In fact, it exacerbates the very power challenge the industry is under pressure to solve.<\/p>\n
That matters right now because the constraints are no longer theoretical. Grid interconnection delays are slowing down projects, electricity costs are under greater scrutiny, and communities are increasingly asking whether AI\u2019s benefits justify its growing energy appetite. The debate cannot focus only on how quickly we build more power. It also has to focus on how intelligently we use the power we already have. Tackling workload volatility with more efficient, purpose-built solutions would not solve every challenge with powering AI, but it would immediately address one of the most overlooked sources of waste.<\/p>\n