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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesFor most GPU-backed LLM services, use waiting requests or another inference-aware metric as the primary autoscaling signal; treat GPU utilization as context, not a direct measure of serving pressure. Queue depth connects more closely to requests waiting and queueing delay, while GPU utilization only tells you how much time the GPU was active. The right threshold is the one that meets your latency and throughput goals under representative traffic—and only if Kubernetes can actually schedule the additional GPU-backed pods.
Choose a signal that reflects serving pressure
LLM autoscaling works best when the trigger describes what the inference server is experiencing, not merely what the hardware is doing. A queue that keeps growing indicates requests are waiting for processing. With continuous batching, however, a low queue can coexist with active inference while the server still has room to admit work. Check the server’s running requests or batch occupancy alongside the queue, and validate every trigger against user-facing latency.
Google Cloud recommends queue-size autoscaling for throughput and cost when latency targets are achievable within the model server’s maximum batch size. Its GKE guidance suggests starting with a queue threshold between three and five, then tuning it against preferred latency; this is a GKE recommendation, not a universal threshold. See GKE autoscaling best practices for LLM inference.
How the main signals compare
| Signal | What it measures and how it reflects saturation | Response and scale-down considerations | Portability and integration |
|---|---|---|---|
| Waiting requests / queue depth | Requests waiting for processing. A sustained or rising queue is a direct sign that demand is outrunning immediately available serving capacity; queue time contributes to end-to-end latency. | Can respond to pressure before latency becomes unacceptable, but is reactive and depends on metric collection and scale-up time. Useful for scale-down when the queue drains, subject to cooldown and stabilization settings. | Inference-aware metrics are runtime-specific. vLLM exposes vllm:num_requests_waiting; verify the metric and labels in the deployed server. |
| Running requests / batch size | Requests undergoing inference or occupying the server’s active batch. Helps show active concurrency and whether the server is approaching its batching capacity. | Can be useful when a latency objective is too strict for queue depth to react in time. A running-request count alone does not reveal whether requests are progressing efficiently. | Metric names and batch semantics vary by serving runtime and version. vLLM exposes running-request metrics; inspect its live /metrics output. |
| KV-cache utilization and preemptions | KV-cache use indicates pressure on a key inference-serving capacity; preemptions can signal memory pressure affecting requests. | Can identify a bottleneck not visible in queue depth alone. Confirm that the signal changes meaningfully with your workload before using it to scale. | NVIDIA’s vLLM metric reference identifies vllm:kv_cache_usage_perc and vllm:num_preemptions; validate names and semantics for your engine version. |
| GPU compute utilization | DCGM_FI_DEV_GPU_UTIL measures GPU duty cycle—the fraction of time the GPU is active—not the amount of useful inference work completed while active. |
A high or low value does not map cleanly to latency or throughput. Use it as contextual evidence or as a trigger only after workload-specific validation; do not assume it is a reliable scale-down signal. | Hardware-oriented and dependent on GPU metrics integration. GKE cautions that duty cycle alone does not measure how much work the GPU accomplishes while active. |
| GPU memory used | DCGM_FI_DEV_FB_USED reports point-in-time framebuffer memory use. |
May help identify a memory-capacity issue, but GKE notes that servers such as TGI and vLLM may preallocate or retain memory, so usage can stay high as traffic falls and fail to guide scale-down. | Depends on GPU monitoring integration and server allocation behavior; interpret it with the engine’s memory-management design. |
Metric names and definitions should be checked against the running server and GPU monitoring setup. NVIDIA’s server metrics documentation describes available server metrics, but the scrape output for your exact serving software and version is authoritative for what your deployment exposes.
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Understand the autoscaling path before choosing a controller
A common architecture is inference server /metrics endpoint → Prometheus scrape → KEDA Prometheus scaler → Kubernetes workload replica count. KEDA can query Prometheus directly; the vLLM Production Stack guide says its KEDA Prometheus scaler does not require Prometheus Adapter. A standard Kubernetes HPA can also use custom or external metrics, but the cluster must provide the corresponding metrics API integration. The basic resource metrics API commonly used for CPU and memory does not itself supply LLM queue depth or NVIDIA GPU duty cycle. See the Kubernetes HorizontalPodAutoscaler API reference.
When an HPA has multiple metrics configured, it calculates a replica recommendation for each and uses the highest recommendation, subject to the configured maximum. This is not an “all signals must cross their targets” rule. Review how each target is defined and aggregated so one metric does not mask pressure or cause unnecessary scale-out.
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KServe documents Prometheus-collected LLM metrics and a push-based OpenTelemetry route. Its InferenceService KEDA autoscaling example is for Standard mode, so check that your KServe deployment mode and release meet the prerequisites before applying it. KServe also documents an LLMInferenceService Workload Variant Autoscaler using inference-specific signals such as queue depth and KV-cache utilization, with HPA or KEDA actuators and optional prefill scaling. Start with the relevant KServe LLM-metrics autoscaling guide and LLMInferenceService configuration guide.
What the published configuration examples do—and do not—establish
The vLLM Production Stack documentation for chart v0.1.11 or later presents a KEDA example with a minimum of one replica, maximum of three, a 15-second polling interval, a 360-second cooldown period, and a Prometheus threshold of five for vllm:num_requests_waiting. The guide describes scaling up when the queue exceeds five pending requests. These are documented example settings, not validated defaults for other models, clusters, or latency targets; exact behavior depends on the trigger/query configuration and KEDA release. The example is in vLLM Production Stack: Autoscaling with KEDA.
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KServe’s Prometheus example is separate: it uses vllm:num_requests_running, a target concurrency of two requests per pod, and a replica range of one to five. Its separate OpenTelemetry example uses a target of four concurrent requests per pod and describes push-based collection as more immediate than polling. Do not combine those examples into a single tested configuration; use the one matching your serving stack and collection path.
Implement and validate autoscaling in seven steps
- Inspect the serving metrics. Identify the inference runtime and check its actual
/metricsendpoint for waiting requests, running requests, KV-cache usage, preemptions, and latency histograms. Confirm labels, units, and whether a metric is per model, replica, or process before writing a query. - Make the metrics available to the scaler. Configure Prometheus to scrape the inference server, or choose a supported OpenTelemetry integration for the serving stack. If using an existing Prometheus with the vLLM Production Stack integration, the guide describes enabling ServiceMonitor resources and pointing the trigger at the actual Prometheus service.
- Select the controller path. Use KEDA’s Prometheus scaler for a direct PromQL trigger; use HPA with custom or external metrics only when the cluster has the required metrics API integration; or use KServe’s integration when its mode and version fit your deployment.
- Start with an inference-level target. For a throughput-and-cost objective, begin by evaluating waiting requests. Consider running requests or batch occupancy when a stricter latency objective means queue-based reaction is too slow; consider KV-cache signals when memory capacity is the limiting factor. Keep GPU duty cycle supplementary unless measurements show that a threshold predicts the serving outcome you care about.
- Bound and tune scaling behavior. Set minimum and maximum replicas, scale-up and scale-down behavior, and cooldown or stabilization settings. Ensure the Prometheus query selects only the intended model and workload and aggregates the right series; unrelated replicas or models must not inflate or hide the signal. Small thresholds can need aggressive scale-up behavior to absorb spikes.
- Load-test the actual traffic shape. Exercise representative prompt lengths, output lengths, concurrency, and bursts. Adjust thresholds until the latency objective and throughput are met without needless replica churn. Monitor vLLM’s end-to-end latency and time-to-first-token histograms as outcomes, test idle periods and scale-down, and record delays from metric observation through pod readiness.
- Verify GPU supply as well as replica limits. A higher replica target does not provision accelerators. Confirm the vendor driver and device plugin advertise schedulable GPU resources—such as
nvidia.com/gpu—and that node autoscaling or reserved capacity can provide the needed devices. Kubernetes documents this resource model in Schedule GPUs.
Account for delays and validate against the latency goal
Autoscaling is reactive when it waits for demand to appear in a metric. Prometheus polling, controller decisions, model loading, node provisioning, and GPU availability can all delay usable capacity. There is no general startup-time or latency guarantee that applies across models and clusters: measure the complete path with your model, serving image, storage, and node configuration. If capacity arrives too late for bursts, keep enough headroom or evaluate predictive scaling or pre-warming.
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Queue depth is a strong starting point when the goal is to balance throughput and cost within a latency target. It cannot guarantee latency below what the server’s maximum batch capacity permits, and queue size does not directly set the number of concurrent requests. For tighter latency goals, test whether batch/concurrency-based scaling reacts sooner. Whichever trigger you choose, judge it by measured latency and throughput, not by a utilization percentage in isolation.
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