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Choose a signal that represents serving pressure
Queue depth counts requests that have arrived but are waiting to be processed. A growing queue is direct evidence that incoming demand is exceeding currently available serving capacity, and waiting time adds to end-to-end latency. For throughput and cost objectives, Google Cloud recommends queue-size autoscaling when the model server can meet the latency target at its maximum batch throughput; that guidance is specific to GKE and still needs validation with the serving stack and traffic in use (GKE LLM inference autoscaling guidance).
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A low queue does not necessarily mean an idle server. With continuous batching, requests can be actively processing while more batch capacity remains available. Conversely, a queue can grow because the engine has reached its practical batch or concurrency limit. Read queue depth alongside active work and latency rather than interpreting it in isolation.
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| Signal | What it measures | How it helps with scaling | Important limitation |
|---|---|---|---|
| Waiting requests / queue depth | Requests waiting for processing; for vLLM, a documented metric is vllm:num_requests_waiting. |
A strong starting trigger for detecting demand beyond current serving capacity. | Queue size does not directly set concurrent requests or guarantee that latency can beat the server’s maximum-batch throughput limit. |
| Running requests or batch occupancy | Requests undergoing inference; vLLM exposes running-request metrics, and batch occupancy can help reveal whether processing capacity is being used. | Useful context when a low queue coexists with active work, or when a strict latency target requires earlier scale-out. | Active requests alone do not show whether requests are waiting or whether user-facing latency is acceptable. |
| KV-cache usage and preemptions | vLLM metrics include vllm:kv_cache_usage_perc and vllm:num_preemptions, which can indicate cache capacity pressure. |
Can identify memory-related constraints that queue depth or GPU duty cycle may not explain. | Verify metric names and meaning in the engine version actually deployed; do not assume every serving runtime exposes the same metrics. |
| GPU compute utilization | DCGM_FI_DEV_GPU_UTIL measures the fraction of time the GPU is active. |
Provides hardware-level context and may be a useful supplementary trigger after workload-specific validation. | It does not measure useful work completed while active, so a utilization threshold does not map cleanly to throughput or inference latency. |
| GPU memory used | DCGM_FI_DEV_FB_USED is a point-in-time measure of framebuffer memory in use. |
May help identify memory pressure or inform scale-up decisions. | For engines such as TGI and vLLM that preallocate or retain memory, it may remain high after demand falls and is therefore a poor scale-down signal. |
| Latency histograms | vLLM exposes end-to-end latency and time-to-first-token histograms. | Show whether a trigger is producing the user-facing outcome the service requires. | Latency is best treated as an outcome to monitor and validate, not as proof that crossing a particular trigger guarantees an SLO. |
The vLLM metric names above are documented in the KServe LLM autoscaling examples and NVIDIA server-metrics reference; confirm exact names and labels at the running server’s /metrics endpoint because available metrics vary by runtime and version.
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How metrics reach the autoscaler
A common architecture is inference server → Prometheus scrape → autoscaler query → workload replica count. The server exposes metrics at /metrics; Prometheus collects them; KEDA’s Prometheus scaler can query the series and scale the Kubernetes workload within configured replica bounds. The vLLM Production Stack documents this route using vllm:num_requests_waiting and says its KEDA Prometheus scaler does not require Prometheus Adapter (vLLM Production Stack KEDA autoscaling).
A standard Kubernetes HorizontalPodAutoscaler can also use custom or external metrics, but the cluster must provide the matching metrics API and integration. The basic Kubernetes resource metrics path covers CPU and memory; it does not by itself supply LLM queue depth or NVIDIA GPU duty-cycle metrics. When an HPA has multiple metrics, it calculates a proposed replica count for each and uses the highest recommendation, subject to the configured maximum. That is not an “all signals must cross their targets” rule (Kubernetes HPA API reference).
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Set up a queue-based trigger, then validate it
- Check the serving metrics. Inspect the runtime’s
/metricsoutput and verify the queue, running-request, cache and latency metrics available in your exact serving image. Record their labels so the autoscaler query can isolate the intended model and workload. - Make the metric available to the controller. Configure Prometheus to scrape the inference server, or use a documented OpenTelemetry integration supported by the selected serving stack. Confirm that the metric appears with the expected labels and updates under load.
- Choose the scaling integration. Use KEDA’s Prometheus scaler for direct PromQL triggers, an HPA with the required custom/external metrics API integration, or a KServe path whose mode and release prerequisites match your deployment. KServe documents Prometheus-collected LLM metrics and a push-based OpenTelemetry route; its InferenceService KEDA example is for Standard mode (KServe autoscaling for LLM metrics).
- Start with an inference-level target. For a throughput-and-cost objective, queue depth is a practical first signal. If latency is stricter or queue reaction is too slow, evaluate batch size or running concurrency; include KV-cache pressure when memory capacity is the constraint. Keep GPU duty cycle as context or a supplementary signal unless tests establish that it tracks the bottleneck for your workload.
- Bound and tune replica behavior. Set minimum and maximum replicas, polling and cooldown behavior, plus scale-up/down policies appropriate to burstiness. Check that the Prometheus query aggregates only the target model and workload: unrelated series can inflate the value or conceal a growing queue.
- Load-test the actual service. Use representative prompt and output lengths, concurrency and burst patterns. Adjust targets until latency and throughput goals are met without unnecessary replica churn. Test idle periods and scale-down as well as spikes, and observe time spent waiting for pods to become schedulable.
- Verify GPU supply separately. Confirm that GPU drivers and the vendor device plugin expose schedulable accelerator resources, such as
nvidia.com/gpu, and that node autoscaling or reserved capacity can supply them. Increasing a replica target does not create GPU capacity (Kubernetes GPU scheduling).
Use documented thresholds as examples, not defaults
The vLLM Production Stack guide for chart version v0.1.11 or later shows an example with minimum one and maximum three replicas, a 15-second KEDA polling interval, a 360-second cooldown, and a Prometheus threshold of five for vllm:num_requests_waiting. Its prose describes scaling up when the queue exceeds five pending requests. These are configuration examples, not measured results or universal recommendations; exact behavior depends on the query, trigger configuration and KEDA release.
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Separately, GKE guidance suggests starting with a queue-size HPA threshold between three and five, then raising it gradually until requests reach the preferred latency. It also advises tuning scale-up behavior for spikes when thresholds are below ten. These are GKE tuning recommendations, not guarantees for other platforms or serving stacks. Queue-based scaling cannot overcome a model server’s maximum-batch throughput ceiling; for latency-sensitive workloads where queue reaction cannot meet the objective, GKE points to batch-size autoscaling as an alternative.
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KServe’s documented Prometheus example tracks vllm:num_requests_running, targets concurrency of two requests per pod, and sets replica bounds from one to five. Its separate OpenTelemetry example targets four concurrent requests per pod and describes push-based collection as more immediate than polling. These are distinct examples; do not combine their values into a configuration that has not been validated (KServe LLM autoscaling examples).
Account for the delay between demand and usable capacity
Autoscaling is reactive when it waits for observed demand. Even after the controller requests another replica, model loading, node provisioning or GPU scarcity can delay the point at which that replica can serve traffic. There is no single startup-time or latency guarantee that applies across models, images, storage paths and clusters, so measure those delays in the actual deployment. If capacity arrives too late for the latency objective, maintain enough headroom or use a suitable predictive or pre-warming design.
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For KServe deployments, check the applicable mode and release-specific setup before choosing its KEDA integration. The KServe LLMInferenceService configuration also describes a Workload Variant Autoscaler using inference-aware signals such as queue depth and KV-cache utilization, with HPA or KEDA actuators and optional prefill scaling (KServe LLMInferenceService configuration).
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