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title Planner Guide
subtitle Configures Planner optimization targets, scaling modes, and PlannerConfig fields for production deployments.

The Dynamo Planner is an autoscaling controller that adjusts prefill and decode engine replica counts at runtime to meet latency SLAs. It reads traffic signals (Prometheus metrics or load predictor output) and engine performance models to decide when to scale up or down.

Forward Pass Metrics (FPM) are per-iteration scheduler records from inference workers. They describe batch composition, queue depth, token counts, and forward-pass duration. The Planner uses these records to tune its performance model from live traffic or to build a regression model when a native AISimulate estimate is unavailable.

For a quick overview, see the Planner overview. For architecture internals, see Planner Design.

Scaling Modes

The planner supports four optimization targets that determine how scaling decisions are made:

  • throughput (default): Uses static thresholds on queue depth and KV cache utilization. No SLA targets or profiling needed. Works out of the box.
  • latency: Same approach as throughput but with more aggressive thresholds — scales up earlier and tolerates less queuing. Ideal for latency-sensitive workloads.
  • load: Uses user-defined prefill queue token thresholds and decode KV utilization thresholds for reactive load-based scaling.
  • sla: Uses the Planner engine-query layer with forward-pass estimates from the AIConfigurator compatibility API in the aisimulate wheel, plus online FPM tuning or FPM regression fallback, to target specific TTFT/ITL values. Supports both throughput-based (predictive) and load-based (reactive) scaling modes. For advanced users who need precise SLA control.

When to use which:

  • Start with throughput (the default) — it works immediately with no configuration.
  • Switch to latency if your workload has strict latency requirements and you prefer to over-provision rather than queue.
  • Use load when you want direct control through prefill queue and decode KV utilization thresholds.
  • Use sla when you want to target specific TTFT/ITL values with native AISimulate estimates, optional bootstrap profiling data, or live FPM warmup.

PlannerConfig Reference

The planner is configured via a PlannerConfig JSON/YAML object. When using the profiler, place this under spec.features.planner in the DGDR spec. Any PlannerConfig field listed below can be set there; DGDR passes that object to the Planner service for validation.

Note

This section covers the most common fields. For every field, sub-object, default, and validation rule, see the Planner Configuration reference.

spec:
  features:
    planner:
      mode: disagg
      backend: vllm
      # optimization_target defaults to "throughput" — works out of the box

For SLA-based scaling:

spec:
  features:
    planner:
      optimization_target: sla
      enable_throughput_scaling: true
      enable_load_scaling: false
      pre_deployment_sweeping_mode: rapid
      mode: disagg
      backend: vllm

To evaluate Planner behavior without changing replica counts, turn on advisory mode:

spec:
  features:
    planner:
      advisory: true

Advisory mode is suggestion-only. The Planner computes recommended replica counts, logs them, exports them as diagnostics, and shows them in HTML reports. The recommendations are not applied as scaling decisions: the Planner does not execute scaling actions or change the deployment.

Optimization Target

Field Type Default Description
optimization_target string throughput throughput: scale based on queue/utilization thresholds. latency: aggressive low-latency thresholds. load: user-defined prefill queue and decode KV utilization thresholds. sla: AISimulate performance modeling with ttft_ms/itl_ms targets.

When optimization_target is throughput, latency, or load, load-based scaling is automatically enabled and throughput-based scaling is disabled. The ttft_ms/itl_ms fields are ignored.

Scaling Mode Fields (SLA mode)

Field Type Default Description
enable_throughput_scaling bool true Enable throughput-based scaling. Only used when optimization_target: sla.
enable_load_scaling bool false Enable load-based scaling. Only used when optimization_target: sla.

At least one scaling mode must be enabled when using optimization_target: sla.

Pre-Deployment Sweeping

Field Type Default Description
pre_deployment_sweeping_mode string rapid How to generate optional bootstrap performance data: rapid (AIC simulation, ~30s), thorough (real GPUs, 2-4h), or none (skip).

SLA mode constructs each role's model through aisimulate_core.sdk.RustForwardPassPerfModel.best_available(config). Set ais_perf_model.roles to the complete AISimulate configuration for each deployed role: prefill and decode for disaggregated deployments, or aggregated for an aggregated deployment. Each role key must match its worker_type.

New configurations default to estimation_mode: auto and fallback_policy: deny. Auto searches op_level, fpm_interpolation, then fpm_regression; deny prevents fallback only when you explicitly select a mode. Without ais_perf_model, the Planner creates a cold fpm_regression model and trains it from self-benchmark or live forward-pass metrics (FPM).

Each role accepts the complete upstream schema, including ordered systems_paths, database_mode, transfer_policy, quantization and speculation settings, and nested estimator_config controls. Explicit estimator controls override the Planner's sampling defaults. fpm_sample_bucket_size: 16 corresponds to a [4, 4] grid; regression and correction may use separate grids. Unknown fields and conflicting worker identities are rejected.

Configure model identity and estimator controls through ais_perf_model.roles. The retired aic_perf_model field and hf_id / parallel-pick shape are rejected.

At startup, the planner always tries to fetch self-benchmark results from the get_perf_metrics Dynamo endpoint. If unavailable, it falls back to rapid-mode AIC interpolation data or profiler-generated data (npz or JSON) at profile_results_dir when configured. These sources are converted to ForwardPassMetrics and used to tune or bootstrap the perf model. With pre_deployment_sweeping_mode: none, the planner can still start; throughput decisions report model_not_ready until native AIS estimates are available or enough live FPMs have warmed the regression fallback.

Configure the role models in a DynamoGraphDeploymentRequest:

spec:
  features:
    planner:
      optimization_target: sla
      mode: disagg
      ais_perf_model:
        roles:
          prefill:
            model: Qwen/Qwen3-32B
            system: h200_sxm
            backend: vllm
            worker_type: prefill
            estimation_mode: auto
            fallback_policy: deny
            tp: 1
          decode:
            model: Qwen/Qwen3-32B
            system: h200_sxm
            backend: vllm
            worker_type: decode
            estimation_mode: auto
            fallback_policy: deny
            tp: 1
            estimator_config:
              fpm_regression:
                sampling:
                  bins_per_axis: [4, 4]
                  max_observations: 64
                min_observations: 5

Throughput-Based Scaling Settings

Field Type Default Description
throughput_adjustment_interval_seconds int 180 Seconds between throughput-based scaling decisions.
max_throughput_scaling_replicas int 8 Maximum replica-count change per component from one throughput observation. The capped target persists until the next throughput observation. GPU and power budgets may reduce it further; endpoint and out-of-band budget recovery take precedence.
throughput_metrics_source string frontend Prometheus traffic source for throughput scaling: frontend reads dynamo_frontend_* metrics from the public Frontend; router reads dynamo_component_router_* metrics from a LocalRouter. Use router for pool-local Planner in GlobalPlanner deployments.
min_endpoint int 1 Minimum endpoints for agg mode. In disagg mode, applies the same minimum to prefill and decode. In prefill or decode mode, supplies the active role when its role-specific field is null. May be 0 for scale-to-zero compatibility.
prefill_min_endpoint int or null null Minimum prefill endpoints for disagg and prefill modes. When set, replaces the prefill value from min_endpoint. Must be at least 1.
decode_min_endpoint int or null null Minimum decode endpoints for disagg and decode modes. When set, replaces the decode value from min_endpoint. Must be at least 1.
max_gpu_budget int 8 Maximum total GPUs the planner may allocate.
ttft_ms float 500.0 TTFT SLA target (ms) for scaling decisions.
itl_ms float 50.0 ITL SLA target (ms) for scaling decisions.

Load-Based Scaling Settings

Field Type Default Description
load_adjustment_interval_seconds int 5 Seconds between FPM tuning updates and load-based scaling decisions. Even when only throughput scaling is enabled, live FPM observations are fed into the perf model at this interval. Must be shorter than throughput_adjustment_interval_seconds.
max_num_fpm_samples int 64 Maximum retained FPM observations for online tuning or regression.
fpm_sample_bucket_size int 16 Number of buckets for observation retirement (must be a perfect square).
load_scaling_down_sensitivity int 80 Scale-down sensitivity 0–100 (0=never, 100=aggressive).
load_min_observations int 5 Minimum observations before making scaling decisions.

General Settings

Field Type Default Description
mode string disagg Planner mode: disagg, prefill, decode, or agg.
backend string vllm Backend: vllm, sglang, trtllm, or mocker.
environment string kubernetes Runtime environment: kubernetes, virtual, or global-planner.
namespace string env DYN_NAMESPACE Dynamo logical/runtime namespace for the deployment.
advisory bool false Suggestion-only mode. Compute, log, export, and report recommended replica counts without executing scaling actions or changing the deployment.

Traffic Prediction Settings

Field Type Default Description
load_predictor string arima Prediction method for request count, ISL, and OSL: constant, arima, kalman, or prophet. Runtime metadata such as KV hit rate and speculative decode accept length uses the latest valid observation instead.
load_predictor_log1p bool false Apply log1p transform to predicted request count, ISL, and OSL data.
prophet_window_size int 50 Window size (seconds) for Prophet predictor.
load_predictor_warmup_trace string null Path to a warmup trace file for bootstrapping predictions.

KV hit rate and speculative decode accept length are runtime engine/router signals, not traffic shape. The planner stores the latest valid observation for each signal and reuses it until a newer valid value arrives. On cold start, missing KV hit rate means no prefix-cache discount, and missing accept length means 1.0.

Kalman Filter Settings

Field Type Default Description
kalman_q_level float 1.0 Process noise for level component.
kalman_q_trend float 0.1 Process noise for trend component.
kalman_r float 10.0 Measurement noise.
kalman_min_points int 5 Minimum data points before Kalman predictions activate.

Diagnostics Reports

Field Type Default Description
report_interval_hours float or null 24.0 Generate an HTML diagnostics report every N hours (simulated time). Set to null to disable periodic report generation.
report_output_dir string ./planner_reports Directory for HTML diagnostics reports.
live_dashboard_port int 8080 Port for the live diagnostics dashboard HTTP server. Set to 0 to disable. When enabled, visit http://host:port/ to view a real-time Plotly report of accumulated snapshots.
control_api_port int 9086 Port for the loopback-only runtime endpoint and GPU budget API. Set to 0 to disable.

Runtime Minimum Endpoint API

The Planner listens on 127.0.0.1:<control_api_port> and supports GET and partial PATCH requests at /v1/min-endpoints. The API has no authentication and is not exposed by a Kubernetes Service. It uses prefill_min_endpoint and decode_min_endpoint in disaggregated mode, the active component's field in single-component mode, and min_endpoint in aggregated mode. Every mode also accepts min_gpu_budget and max_gpu_budget. A response includes the active endpoint fields and both GPU budgets. Updates are process-local, are not written back to the Planner ConfigMap, and apply to the next planner tick.

In Kubernetes, port-forward to the Planner pod and patch the active mode's field. The following example updates a Planner in disagg mode:

kubectl port-forward pod/<planner-pod> 9086:9086
curl http://127.0.0.1:9086/v1/min-endpoints
curl --request PATCH http://127.0.0.1:9086/v1/min-endpoints \
  --header 'Content-Type: application/json' \
  --data '{"prefill_min_endpoint": 8, "decode_min_endpoint": 8, "min_gpu_budget": 16, "max_gpu_budget": 64}'

The update is atomic. Use -1 to disable either GPU budget. The Planner rejects malformed values, fields that are inactive for the current mode, a min_gpu_budget greater than max_gpu_budget when both are enabled, and minimum footprints that exceed max_gpu_budget or the configured power budget. The same checks run at startup, so an infeasible minimum configuration fails before the Planner enters its tick loop. Scale-up has no per-component maximum endpoint setting; the existing GPU, power, Global Planner, and cluster-capacity limits remain the upper bounds.

Warning

If a scaling submission loses its acknowledgement after the Planner stops waiting for it, a later submission error leaves the outcome unknown. The Planner then stops automatic effect submission to avoid duplicating an action that the remote service might have applied. Runtime GET and PATCH remain available. Verify the deployment's desired and Ready replica counts, then restart the Planner to resume automatic submission.

The same diagnostic signals surfaced in these reports are also exported as Prometheus metrics under the dynamo_planner_* prefix—for example estimated TTFT/ITL (dynamo_planner_estimated_ttft_ms, dynamo_planner_estimated_itl_ms), recommended replica counts (dynamo_planner_predicted_num_prefill_replicas, dynamo_planner_predicted_num_decode_replicas), per-engine capacity and FPM queue depths, and load/throughput scaling decision enums.

The Replica Counts plot overlays actual prefill/decode replicas with discrete recommendation markers for the Planner's recommended prefill/decode replicas. When advisory: true, these recommended counts are suggestions only; the Planner records what it would do without applying the change.

Scheduling / plugin pipeline

The planner runs through the builtin plugin pipeline by default. The base pipeline cadence lives under the scheduling sub-tree of PlannerConfig; plugin registration, transport, and auth settings live under plugin_registration.

Field Type Default Description
scheduling.scale_interval_seconds float gcd of enabled builtin intervals Base pipeline cadence. The pipeline wakes once per interval; each plugin's execution_interval_seconds decides whether that plugin fires on the tick. By default, the cadence is the gcd of load_adjustment_interval_seconds and, when throughput scaling is enabled, throughput_adjustment_interval_seconds, preserving existing config fire times.
scheduling.tick_max_duration_seconds float 30.0 Outer deadline wrapping the full plugin pipeline. Exceeding it aborts the tick; the next tick runs from a clean state.
plugin_registration.transport.request_timeout_seconds float 5.0 Per-plugin RPC timeout. Plugins exceeding this raise PluginTimeoutError; the stage continues with the remaining plugins.

Existing planner fields still drive the builtin plugins:

  • load_adjustment_interval_seconds schedules builtin_load_propose, which reads FPM and worker-count observations and applies the current load-based algorithm.
  • throughput_adjustment_interval_seconds schedules builtin_load_predict and builtin_throughput_propose. The throughput proposer requires the prediction from the same tick, so it only fires when the predict plugin fires.
  • When both builtins propose targets in the same tick, load-based scaling runs after throughput-based scaling and preserves the existing behavior: throughput updates the lower-bound replicas, then load-based scaling can adjust above that floor and apply the global GPU budget clamp.
  • After the plugin pipeline finishes, the planner applies the same final effective component minimums and GPU-budget safety checks to built-in and external-plugin targets before scaling the deployment.

DGDR example

apiVersion: nvidia.com/v1beta1
kind: DynamoGraphDeploymentRequest
metadata:
  name: my-deployment
spec:
  model: Qwen/Qwen3-0.6B
  features:
    planner:
      optimization_target: sla
      enable_load_scaling: true
      ttft: 200.0
      itl: 10.0
      pre_deployment_sweeping_mode: rapid
      scheduling:
        tick_max_duration_seconds: 30.0
      plugin_registration:
        transport:
          request_timeout_seconds: 5.0

Integration with Profiler

When the profiler runs with planner enabled, it:

  1. Selects the best prefill and decode engine configurations
  2. Generates engine performance data (prefill TTFT vs ISL, decode ITL vs KV-cache utilization)
  3. Saves the PlannerConfig and performance data into separate Kubernetes ConfigMaps
  4. Adds the planner service to the generated DGD, configured to read from those ConfigMaps

The planner receives its config via --config /path/to/planner_config.json which is mounted from the planner-config-XXXX ConfigMap. When thorough bootstrap data is generated, profiling data is mounted from the planner-profile-data-XXXX ConfigMap.

See the Profiler Guide for the full profiling workflow and how to configure pre-deployment sweeping.

Hierarchical Deployments

If you want one public endpoint for a model but multiple private DGDs optimized for different request classes, use a hierarchical deployment:

  • one control DGD with Frontend, GlobalRouter, and GlobalPlanner
  • one or more prefill pool DGDs
  • one or more decode pool DGDs

In the current workflow, run profiling independently for each intended pool, then compose the final control DGD plus pool DGDs manually. See the Global Planner Guide.

See Also