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    you may not use this file except in compliance with the License.
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Planner
=======

The planner monitors the state of the system and adjusts workers to ensure that the system runs efficiently.

Currently, the planner can scale the number of vllm workers up and down based on the kv cache load and prefill queue size:

Key features include:

* **Load-based scaling** that monitors KV cache utilization and prefill queue size to make scaling decisions
* **SLA-based scaling** that uses predictive modeling and performance interpolation to proactively meet TTFT and ITL targets
* **Multi-backend support** for both local (Circus) and Kubernetes environments
* **Graceful scaling** that ensures no requests are dropped during scale-down operations

.. list-table::
   :widths: 20 5 75
   :header-rows: 1

   * -
     -
     - Feature
   * - **Backend**
     - ✅
     - Local
   * -
     - ✅
     - Kubernetes
   * - **LLM Framework**
     - ✅
     - vLLM
   * -
     - ❌
     - TensorRT-LLM
   * -
     - ❌
     - SGLang
   * -
     - ❌
     - llama.cpp
   * - **Serving Type**
     - ✅
     - Aggregated
   * -
     - ✅
     - Disaggregated
   * - **Planner Actions**
     - ✅
     - Load-based scaling up/down prefill/decode workers
   * -
     - ✅
     - SLA-based scaling up/down prefill/decode workers [1]_
   * -
     - ❌
     - Adjusting engine knobs

.. [1] Supported with some limitations.

.. toctree::
   :hidden:

   Pre-Deployment Profiling <pre_deployment_profiling.md>
   Load-based Planner <load_planner.md>
   SLA-based Planner <sla_planner.md>