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Production AI performance depends on the whole workload path, not just the number of GPUs. Data preparation, CPU preprocessing, inference, post-processing, and application work all need resources—and the handoffs between them must keep data and model artifacts moving. For a Kubernetes platform team, the more useful question is not only “How many GPUs does this workload need?” but “What does each stage need, and what depends on what?”
Why GPU capacity alone is an incomplete plan
A GPU may perform the densest computation in an AI workload, but it cannot work on data that has not reached it. CPU work, memory capacity, storage access, networking, scheduling, and application logic all affect end-to-end latency, throughput, and startup time. If any stage cannot keep up, an accelerator can sit idle even when the cluster appears to have plenty of GPU capacity.
This is the central idea behind heterogeneous infrastructure: CPUs, GPUs or other accelerators, memory, storage, and networks are complementary parts of one system. CNCF’s September 4, 2026 article by Kasia Hilborne, affiliated with Vultr, puts it this way: “The key shift is conceptual: AI infrastructure is not a collection of GPUs with supporting services around them.” Read the CNCF article.
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Map the actual path a request takes before deciding how much of each resource to provision. A typical inference flow can include:
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- Data preparation: Load, validate, or transform inputs and retrieve any required context.
- CPU preprocessing: Tokenize or otherwise prepare data in a form the model can consume.
- GPU inference: Run the model’s compute-intensive operations.
- CPU post-processing: Decode, format, filter, or otherwise prepare the model output.
- Application work: Handle request routing, orchestration, business logic, and response delivery.
The boundaries are not always separate services; some work may happen inside an application or inference server. The useful question is whether each stage can supply the next at the rate the workload requires. Slow retrieval or preprocessing can starve the GPU. Slow post-processing or application work can keep users waiting after GPU computation is complete.
Match each stage to its resource needs
Start with the work performed at each stage, then identify the resources and dependencies that can constrain it.
| Workload stage or concern | Common resource role | What to examine |
|---|---|---|
| Data preparation and retrieval | CPU, memory, storage, and network | Whether inputs and context can be found and delivered quickly enough. |
| Tokenization and preprocessing | CPU and memory | Whether prepared inputs arrive at the accelerator at the required rate. |
| Model inference | GPU or another suitable accelerator, plus memory | Compute capacity, model and working-set memory, and the workload’s latency or throughput needs. |
| Post-processing and application logic | CPU and memory, with network for response handling | Whether output handling, routing, and application work delay completion. |
| Model loading and startup | Storage, network, memory, and accelerator availability | How quickly model artifacts reach the workload and become ready to serve. |
This is a planning framework, not a hardware sizing table: the CNCF article provides no benchmark values or winning configuration. Measure the demands of the workload and its service requirements rather than assuming a fixed CPU-to-GPU ratio.
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Treat LLM prompt processing and token generation as distinct work
Large language model inference is not necessarily one uniform operation. Prompt processing and token generation can have different compute and memory requirements. A capacity plan that treats them as indistinguishable may overlook where memory pressure or compute demand actually arises.
Separate these phases in workload analysis where the serving stack and telemetry allow it. Evaluate them against the same end-to-end goals—such as latency and throughput—but avoid assuming that one aggregate GPU utilization number explains the behavior of both.
Trace bottlenecks across handoffs
GPU utilization is a clue, not a diagnosis. Low utilization can mean that demand is low, but it can also mean that an upstream stage is not feeding the accelerator. Data access, CPU preprocessing, or scheduling may be limiting progress. Conversely, high utilization does not by itself establish that a workload meets its latency or throughput requirements.
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Correlate application and infrastructure telemetry so the team can see where time is spent and whether delays line up with resource constraints. Useful evidence includes request latency and throughput alongside CPU, GPU, memory, storage, and network behavior, plus workload startup time when model loading is material. The goal is to identify the stage or handoff that misses the workload’s requirement—not to force every resource to 100% utilization.
Express accelerator needs through the orchestration layer
Kubernetes is a common orchestration layer for production AI workloads. Specialized devices still require a clear way for workloads to request and use them. Dynamic Resource Allocation (DRA) is an example of a more flexible, declarative resource-request model for specialized devices; whether it fits a deployment depends on its Kubernetes environment and device integration.
Keep the resource request aligned with the workload’s stages and dependencies. Accelerator allocation does not replace planning for CPU, memory, storage, networking, model delivery, or the application work surrounding inference.
Use a workload-first capacity review
- Draw the request path: Include retrieval and preparation, inference, post-processing, and application work, noting which components are shared or separate.
- Record requirements by stage: Define the relevant latency, throughput, and startup expectations, then identify the resources each stage consumes.
- Mark dependencies and handoffs: Track where data, context, and model artifacts move, and what must finish before the next stage can proceed.
- Observe the complete path: Correlate application timings with CPU, GPU, memory, storage, network, and scheduling signals.
- Change the constrained part: Add or adjust capacity where evidence shows a bottleneck, then verify that the end-to-end requirement improves.
This method also makes infrastructure comparisons more meaningful: compare options by stage-level resource fit, handoff performance, startup behavior, and operational model—not by accelerator count alone. Cloud GPU instances or GPU clusters can provide accelerator capacity when needed, but their existence does not remove the need to assess the surrounding data path. For example, Vultr documents Cloud GPU virtual machines with NVIDIA and AMD GPUs for AI, machine learning, and HPC workloads, and GPU-fabric clusters for AI/ML training and distributed inference that require high-bandwidth, low-latency communication using RoCE or InfiniBand. These are service descriptions, not evidence that a particular configuration will meet a given workload’s requirements: Vultr Cloud GPU overview and Vultr cluster guide.
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