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Set CPU and host-memory requests and limits from measurements of your actual inference workload, then request the GPU resource advertised by your cluster’s device plugin. Requests influence where Kubernetes schedules the Pod; limits are enforcement boundaries. A GPU allocation reserves a schedulable device, not a specified amount of VRAM, so the model, context length, concurrency and GPU type must also fit.
What requests and limits mean for an inference Pod
Kubernetes uses resource requests when deciding whether a Pod fits on a node. For memory, usage above a Pod’s request is not included when the scheduler decides whether another Pod fits. On Linux, container limits are commonly enforced through cgroups. A limit set without a request can become the request by default, so inspect the effective Pod settings rather than assuming an omitted request means the scheduler reserves nothing. Kubernetes documents resource management for Pods and containers.
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| Resource setting | What it does | What it does not guarantee |
|---|---|---|
| CPU request | Informs scheduling about the CPU the Pod needs. | It does not establish a universal safe CPU limit for a model. |
| Memory request | Informs scheduling about the Pod’s memory needs. | It does not make memory use above the request count when fitting another Pod. |
| CPU or memory limit | Sets an enforcement boundary for the container. | It does not determine the amount required for a particular traffic pattern. |
| GPU resource | Requests an integer device resource advertised to Kubernetes. | A device count does not express the device’s VRAM capacity or prove that a model fits. |
For a GPU-backed language model, host RAM and GPU memory are separate constraints. Host memory must cover the serving process and its host-side work; GPU memory must accommodate the model and runtime’s GPU-side needs for the chosen workload. Kubernetes’ GPU resource request handles device allocation, not that capacity calculation.
How to size CPU, memory and GPU for the workload
- Define the workload. Record the model and quantization, serving engine and version, target context length, expected concurrent sequences, batching settings, input-processing needs, and whether tensor or pipeline parallelism is enabled. These determine what a representative test needs to exercise.
- Select the GPU class and resource name. Check which GPU types are available, how much device memory they have, and which extended resource name the installed device plugin advertises. The common NVIDIA resource name is
nvidia.com/gpu, but use the name present in your cluster rather than assuming it. - Measure CPU and host memory under realistic load. Load the actual model and test representative prompt and generation lengths, concurrency, and traffic ramp-up. Include model loading, tokenization or other input processing, runtime overhead, and peak traffic when setting requests and limits.
- Observe GPU behavior and serving health. During the same test, track GPU utilization and memory, startup and readiness, latency, throughput, CPU throttling, host memory, and failures or restarts. A Pod that schedules is not necessarily able to load the model or serve the target traffic reliably.
- Adjust based on the failure mode. If the Pod cannot fit or start, check the requested node class, device availability, host memory, GPU memory and engine settings. If it starts but misses its traffic or reliability goals, review measured CPU and memory pressure, throttling, concurrency and context caps, and GPU topology. Retest after changes; Kubernetes does not infer a safe resource recipe from the model name.
Leave headroom for peak traffic and non-model overhead. Choose limits in line with your isolation and failure policy, and check the actual effects of those limits in testing. The Kubernetes documentation does not publish a universal CPU or memory recipe for LLM inference.
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How GPU requests and limits work
GPU resources are extended resources managed by a device plugin. Under Kubernetes’ documented GPU scheduling model, you may specify a GPU limit alone, in which case it also serves as the request, or set equal request and limit values. A GPU request without a limit is invalid. See the Kubernetes GPU scheduling rules.
Device-plugin resources are integer quantities; under the documented model they cannot be overcommitted, and a device-plugin-managed device cannot be shared between containers. The installed plugin and its configuration determine what resource is advertised. Kubernetes explains device-plugin resources here.
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If nodes have different GPU types or memory capacities, constrain placement with suitable node labels, selectors or affinity. Otherwise, a Pod requesting one GPU could be eligible for a device that does not meet the model’s needs. Check node allocatable capacity, labels, taints, affinity rules and device-plugin health as well as the resource count.
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The official vLLM Kubernetes guide includes an NVIDIA example for a Mistral-7B-Instruct-v0.3 manifest. Its resource values are example settings, not a sizing guarantee:
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| Setting in the guide’s example | Value |
|---|---|
| CPU request | 2 |
| Memory request | 6G |
| CPU limit | 10 |
| Memory limit | 20G |
nvidia.com/gpu request and limit |
1 each |
Memory-backed shared-memory volume at /dev/shm |
sizeLimit: 2Gi |
The guide comments that vLLM needs host shared memory for tensor-parallel inference. These figures apply to that guide’s example manifest; they are not recommendations for every model, GPU, context length, concurrency level, vLLM release or cluster.
Check placement constraints and namespace policies
A valid Pod specification can still fail to schedule if the node lacks allocatable requested resources, the required GPU resource is unavailable, or placement rules exclude otherwise suitable nodes. Namespace policies can also affect the effective constraints:
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- ResourceQuota can cap aggregate namespace requests, including GPUs.
- LimitRange can set defaults and impose per-container or per-Pod resource bounds.
- GPU node labels, taints and affinity must match the hardware the workload needs.
For clusters considering Dynamic Resource Allocation, check the cluster release and feature configuration before relying on it. The Kubernetes DRA API page says extended-resource allocation by DRA is stable since Kubernetes v1.37, enabled by default, and first available in v1.34. Consult the DRA API documentation for the relevant release.
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If the Pod uses a memory-backed emptyDir volume, set an explicit sizeLimit and account for it in the container’s memory budget. Kubernetes warns that without a size limit, such a volume can consume up to the memory limit—or potentially node memory when no memory limit is set. This is especially relevant when configuring shared memory for inference.
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