Solving GPU starvation for enterprise AI workloads

This article explains why enterprise GPUs sit idle and how orchestration fixes it. This applies to on-prem and hybrid enterprise HPC environments running GPU-accelerated AI, simulation and EDA workloads. 

Right now, in most data centers, a GPU that costs more than most cars is sitting idle. It’s not broken. It’s not powered down. It’s just waiting. 

That’s the quiet, expensive irony at the center of enterprise AI right now. Companies are racing to buy the most powerful accelerators on the market, and the market is racing right alongside them. Gartner reports that AI-optimized Infrastructure-as-a-Service spending is projected to climb from $21 billion in 2025 to $42 billion in 2026, and onto $66 billion in 2027. That’s one of the largest capital bets in enterprise technology history. 

And yet, for a lot of organizations, the GPUs at the center of that bet spend a surprising amount of time doing nothing at all. Not because the AI models aren’t ready. Not because the ambition isn’t there. The hardware and compute power are simply stuck waiting on everything around it: data that hasn’t been prepped, pipelines that can’t keep pace, fragmented scheduling that creates compute silos. 

This is GPU starvation, and it’s quietly eating the ROI out of enterprise AI. Every idle GPU hour is a wasted hour of compute you already paid for. Multiply that across a fleet of accelerators and a fiscal year, and you’ve got a very expensive paperweight problem. 

The instinct is to fix it by buying more GPUs. It won’t work. If your pipeline can’t feed the GPUs you have, it won’t feed the ones you buy next either. The real fix isn’t more silicon. It’s making the silicon you already deliver a more complete ROI. 

Fragmented scheduling starves your GPUs 

Let’s define the term properly. GPU starvation happens when an accelerator, the fastest, most expensive piece of hardware in the building, sits idle waiting on something slower to catch up. It’s not a hardware failure. It’s a workflow failure and it’s shockingly common. 

Three things typically cause it: 

CPU pre-processing can’t keep pace. Before a GPU can train or run a model, data has to be loaded, decoded and prepared. That work runs on the CPU, and CPUs are slower. If preprocessing lags, the GPU finishes its job and then just waits for more work to show up. 

Data pipeline I/O becomes the chokepoint. Storage and network throughput often can’t move data fast enough to keep pace with GPU compute speed. The GPU is ready. The data isn’t. 

Fragmented queues leave scheduling broken. In a lot of enterprise environments, AI workloads run on their own scheduler, completely separate from the HPC scheduler managing simulation, EDA and other traditional workloads. Neither system has visibility into the other, so neither can fill the other’s idle gaps. 

This isn’t a fringe theory. A recent report from Cast AI found that average enterprise GPU utilization sits at just 5 percent, meaning 95 percent of provisioned GPU capacity is sitting idle. Worse, this means that organizations are paying for roughly 20 times more GPU capacity than their workloads use. This represents millions of GPUs and billions of dollars in idle capacity. 

So why is this so common, and why is it such a difficult challenge to solve? 

Most enterprise compute environments were created to manage one kind of workload at a time. HPC schedulers were built for steady, predictable simulation and EDA jobs. Then AI came along with an entirely different profile: bursty, GPU-hungry and often managed by a completely separate team using completely separate tools. The result is two worlds that never talk to each other, sitting on top of the same physical infrastructure, each one blind to what the other is doing. 

Left unresolved, the cost compounds. Slower time-to-insight. Budget overruns that make finance nervous about the next AI investment. And a widening gap between the organizations that get AI working at scale and the ones still waiting on their own infrastructure to catch up. 

How intelligent orchestration solves GPU starvation 

To put it simply, solving this challenge requires efficient, intelligent orchestration. That’s exactly what Siemens HPCWorks was built to deliver. 

The core idea is simple to state and hard to execute without the right platform: AI shouldn’t run in its own isolated cloud corner. It should run alongside simulation, EDA and Digital Twin workloads on one enterprise-wide compute platform, where every job, regardless of type, can be scheduled, tracked and optimized together. 

Here’s how HPCWorks tackles each piece of the problem: 

Computational workload management intelligently co-schedules AI jobs alongside simulation and EDA workloads, optimizing resource distribution to eliminate idle compute cycles. This is the direct answer to the fragmented queue problem. 

Dependency and flow management gives administrators a clear view of how workloads depend on each other, so bottlenecks in CPU pre-processing or data staging get flagged and addressed before they leave a GPU sitting idle. This targets the exact failure mode behind the utilization numbers above, except now it’s managed at the orchestration layer across your entire compute estate, not just within a single training job. 

HPC operations management, analytics and optimization give you full visibility into utilization across the board. You can see precisely where GPUs stall, whether it’s I/O, scheduling or something else, and act on it with data instead of guesswork. 

Dynamic hybrid cloud scaling lets workloads flex across on-prem and cloud environments without recreating the silo problem you’re trying to eliminate. Capacity moves to where the work is, not the other way around. 

Last but not least, agentic HPC helps engineers and researchers accurately estimate the resources their workloads require. This eliminates the guesswork and the trial-and-error cycles that often lead to failed jobs, removing yet another source of delay. 

Last but not least, an intuitive end user portal means engineers and researchers can run complex workloads without waiting in an IT queue, removing yet another source of delay. 

None of this is a hardware upgrade. It’s putting the hardware you already own to full use. 

And the payoff is well documented. A Department of Energy-funded study from Hyperion Research found that across 763 HPC projects, every dollar invested in HPC generated an average of $47 dollars in profits or cost savings. However, that return isn’t automatic. It only shows up when the compute behind it is actually running, not sitting idle waiting on a data pipeline. Fixing the throughput problem is what turns that number from a theoretical benchmark into your actual outcome. 

Explore Siemens HPCWorks for optimizing enterprise AI workloads. 

Frequently asked questions 

What is GPU starvation? GPU starvation occurs when a GPU sits idle waiting on upstream processes, such as CPU data preprocessing or storage I/O, instead of actively computing. It results in expensive hardware running well below its potential, even though the GPU itself is fully functional. 

What causes GPU starvation in enterprise AI infrastructure? The most common causes are CPU pre-processing that can’t keep pace with GPU speed, data pipeline I/O limitations and fragmented job scheduling between AI and traditional HPC workloads.  

Can enterprises fix GPU utilization without buying more GPUs? Yes. Co-scheduling AI workloads alongside existing simulation, EDA and Digital Twin jobs, combined with better dependency and pipeline management, raises utilization on hardware already in place. Buying additional GPUs does not resolve underlying scheduling or pipeline inefficiencies. 

Why shouldn’t AI workloads run in an isolated cloud environment? Isolating AI in its own cloud silo prevents shared visibility and duplicates infrastructure, leaving GPU capacity stranded instead of shared across simulation, EDA and Digital Twin workloads that could otherwise use it. 

How does Siemens HPCWorks address GPU starvation? Siemens HPCWorks unifies job scheduling, workload dependency management and analytics across AI, simulation and EDA workloads on a single enterprise-wide platform. This closes the coordination gaps that leave GPUs idle and increases throughput on existing hardware. 

What is the return on investment for HPC infrastructure optimization? A Department of Energy-funded study from Hyperion Research of 763 successful HPC projects found an average return of $47 dollars in profits or cost savings for every $1 invested in HPC. That return depends on the infrastructure actually running at full utilization. 

Is GPU starvation a recognized industry problem? Yes. A report analyzing telemetry from tens of thousands of Kubernetes clusters across major cloud providers found average enterprise GPU utilization at just 5 percent, with organizations paying for about 20 times more GPU capacity than their workloads use.