Planner Guide

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.

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 Rust engine performance shim with native AIC estimates when available, 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 AIC 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, this is placed under the features.planner section of the DGDR spec:

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

For SLA-based scaling:

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

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

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features:
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  planner:
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    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

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)

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

Pre-Deployment Sweeping

SLA mode uses the Rust engine performance shim. If aic_perf_model is present, the planner initializes the shim with native AIC model identity and engine limits. Unsupported native AIC configs automatically fall back to observed-FPM regression in the shim. If aic_perf_model is absent, the shim starts as an FPM regression model and becomes ready after enough self-benchmark or live FPM observations.

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 AIC is available or enough live FPMs have warmed the regression fallback.

Manual native AIC perf-model config:

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features:
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  planner:
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    optimization_target: sla
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    aic_perf_model:
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      hf_id: nvidia/Llama-3.1-8B-Instruct-FP8
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      system: h200_sxm
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      backend: vllm
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      prefill_pick: {tp: 1, pp: 1, dp: 1, moe_tp: 1, moe_ep: 1}
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      decode_pick: {tp: 1, pp: 1, dp: 1, moe_tp: 1, moe_ep: 1}

Throughput-Based Scaling Settings

Load-Based Scaling Settings

General Settings

Traffic Prediction Settings

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

Diagnostics Reports

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.

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 min_endpoint and GPU-budget safety checks to builtin and external-plugin targets before scaling the deployment.

DGDR example

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

• Planner overview — Why LLM inference needs a different autoscaler
• Planner Design — Architecture and algorithm internals
• Planner Examples — DGDR YAML examples, sample configurations, advanced patterns
• Global Planner Guide — Multi-DGD coordination, shared GPU budgets, single-endpoint multi-pool deployments
• Profiler Guide — How profiling data is generated