Configuration

Everything goes in the ScaledObject trigger metadata. No config files or extra CRDs needed.

Parameters

Parameter	Description	Default
profile	Pre-built scaling profile name	(none)
metricType	GPU metric to scale on	gpu_utilization
targetValue	Target metric value for scaling	80
targetGpuUtilization	Shorthand for GPU utilization target	(none)
targetMemoryUtilization	Shorthand for VRAM utilization target	(none)
activationThreshold	Value below which scale-to-zero activates	0
gpuIndex	Specific GPU index to monitor	-1 (all GPUs)
aggregation	Multi-GPU aggregation: max, min, avg, sum	max
pollIntervalSeconds	Metric polling interval	10

Scaling Profiles

Profiles bundle defaults for common workloads. Override any parameter in the trigger metadata.

Profile	Primary Metric	Target	Activation	Use Case
vllm-inference	Memory %	80	5	vLLM / LLM serving with scale-to-zero
triton-inference	GPU Util	75	10	NVIDIA Triton Inference Server
training	GPU Util	90	0	Training jobs (no scale-to-zero)
batch	Memory %	70	1	Batch inference with aggressive scale-down

Using a profile

triggers:
  - type: external
    metadata:
      scalerAddress: "keda-gpu-scaler.keda.svc.cluster.local:6000"
      profile: "vllm-inference"

Overriding a profile parameter

triggers:
  - type: external
    metadata:
      scalerAddress: "keda-gpu-scaler.keda.svc.cluster.local:6000"
      profile: "vllm-inference"
      targetValue: "90"          # override the default 80

Using raw metrics (no profile)

triggers:
  - type: external
    metadata:
      scalerAddress: "keda-gpu-scaler.keda.svc.cluster.local:6000"
      metricType: "gpu_utilization"
      targetValue: "85"
      activationThreshold: "10"
      gpuIndex: "0"
      aggregation: "max"

Multi-GPU Aggregation

On multi-GPU nodes, aggregation controls how per-GPU values are reduced to one number:

• max (default) — scale when any GPU hits the threshold. Good for inference where one hot GPU means overload.
• avg — scale on average utilization. Good for training where GPUs should be evenly loaded.
• min — scale when the least-loaded GPU hits the threshold. Conservative.
• sum — total utilization. Useful for capacity-based decisions.

Scale-to-Zero

Set activationThreshold to enable scale-to-zero. When all GPU metrics drop below this value, KEDA reports the scaler as inactive and scales the deployment to zero replicas.

triggers:
  - type: external
    metadata:
      scalerAddress: "keda-gpu-scaler.keda.svc.cluster.local:6000"
      metricType: "gpu_utilization"
      targetValue: "80"
      activationThreshold: "5"    # scale to zero when GPU util < 5%

Server Flags

These flags configure the scaler binary itself (passed via args in the DaemonSet or Helm values):

Flag	Description	Default
--port	gRPC server port	6000
--metrics-port	Prometheus HTTP metrics port (0 to disable)	9090
--log-level	Log level: debug, info, warn, error	info

Helm Values

grpc:
  port: 6000

metrics:
  enabled: true    # set to false to disable Prometheus endpoint
  port: 9090

logLevel: info

Prometheus Metrics

When --metrics-port is non-zero, an HTTP server exposes /metrics (Prometheus format) and /healthz. This is optional and does not affect the KEDA scaling path.

Metric	Type	Labels	Description
keda_gpu_scaler_gpu_utilization_percent	Gauge	gpu_index, gpu_uuid, gpu_name	GPU compute utilization
keda_gpu_scaler_gpu_memory_used_bytes	Gauge	gpu_index, gpu_uuid, gpu_name	GPU memory in use
keda_gpu_scaler_gpu_memory_total_bytes	Gauge	gpu_index, gpu_uuid, gpu_name	Total GPU memory
keda_gpu_scaler_gpu_temperature_celsius	Gauge	gpu_index, gpu_uuid, gpu_name	GPU temperature
keda_gpu_scaler_gpu_power_draw_watts	Gauge	gpu_index, gpu_uuid, gpu_name	GPU power draw
keda_gpu_scaler_collections_total	Counter	—	Total NVML collection calls
keda_gpu_scaler_collection_errors_total	Counter	—	Failed NVML collections
keda_gpu_scaler_collection_duration_seconds	Histogram	—	NVML collection latency
keda_gpu_scaler_scaler_requests_total	Counter	method	gRPC requests by method
keda_gpu_scaler_scaler_request_errors_total	Counter	method	gRPC errors by method

Examples

Check deploy/examples/ for ScaledObject manifests:

• vllm-scaledobject.yaml — vLLM inference with scale-to-zero
• custom-gpu-utilization.yaml — raw GPU utilization scaling