ai-dynamo · tedzhouhk · Oct 27, 2025 · Oct 24, 2025 · Oct 24, 2025 · Oct 24, 2025
diff --git a/README.md b/README.md
@@ -179,7 +179,7 @@ Rerun with `curl -N` and change `stream` in the request to `true` to get the res
 Dynamo provides comprehensive benchmarking tools to evaluate and optimize your deployments:
 
 - **[Benchmarking Guide](docs/benchmarks/benchmarking.md)** – Compare deployment topologies (aggregated vs. disaggregated vs. vanilla vLLM) using AIPerf
-- **[Pre-Deployment Profiling](docs/benchmarks/pre_deployment_profiling.md)** – Optimize configurations before deployment to meet SLA requirements
+- **[SLA-Driven Dynamo Deployments](docs/planner/sla_planner_quickstart.md)** – Optimize your deployment to meet SLA requirements
 
 # Engines
 

diff --git a/components/backends/trtllm/deploy/README.md b/components/backends/trtllm/deploy/README.md
@@ -53,7 +53,7 @@ Advanced disaggregated deployment with SLA-based automatic scaling.
 - `TRTLLMPrefillWorker`: Specialized prefill-only worker
 
 > [!NOTE]
-> This deployment requires pre-deployment profiling to be completed first. See [Pre-Deployment Profiling](../../../../docs/benchmarks/pre_deployment_profiling.md) for detailed instructions.
+> This deployment requires pre-deployment profiling to be completed first. See [Pre-Deployment Profiling](../../../../docs/benchmarks/sla_driven_profiling.md) for detailed instructions.
 
 ## CRD Structure
 

diff --git a/components/backends/vllm/deploy/README.md b/components/backends/vllm/deploy/README.md
@@ -99,7 +99,7 @@ We have public images available on [NGC Catalog](https://catalog.ngc.nvidia.com/
 
 ### Pre-Deployment Profiling (SLA Planner Only)
 
-If using the SLA Planner deployment (`disagg_planner.yaml`), follow the [pre-deployment profiling guide](../../../../docs/benchmarks/pre_deployment_profiling.md) to run pre-deployment profiling. The results will be saved to the `dynamo-pvc` PVC and queried by the SLA Planner.
+If using the SLA Planner deployment (`disagg_planner.yaml`), follow the [pre-deployment profiling guide](../../../../docs/benchmarks/sla_driven_profiling.md) to run pre-deployment profiling. The results will be saved to the `dynamo-pvc` PVC and queried by the SLA Planner.
 
 ## Usage
 

diff --git a/components/src/dynamo/planner/utils/perf_interpolation.py b/components/src/dynamo/planner/utils/perf_interpolation.py
@@ -28,7 +28,7 @@
 
 MISSING_PROFILING_DATA_ERROR_MESSAGE = (
     "SLA-Planner requires pre-deployment profiling results to run.\n"
-    "Please follow /docs/benchmarks/pre_deployment_profiling.md to run the profiling first,\n"
+    "Please follow /docs/benchmarks/sla_driven_profiling.md to run the profiling first,\n"
     "and make sure the profiling results are present in --profile-results-dir."
 )
 

diff --git a/deploy/utils/README.md b/deploy/utils/README.md
@@ -119,7 +119,7 @@ python3 -m deploy.utils.download_pvc_results \
 
 For complete benchmarking and profiling workflows:
 - **Benchmarking Guide**: See [docs/benchmarks/benchmarking.md](../../docs/benchmarks/benchmarking.md) for comparing DynamoGraphDeployments and external endpoints
-- **Pre-Deployment Profiling**: See [docs/benchmarks/pre_deployment_profiling.md](../../docs/benchmarks/pre_deployment_profiling.md) for optimizing configurations before deployment
+- **Pre-Deployment Profiling**: See [docs/benchmarks/sla_driven_profiling.md](../../docs/benchmarks/sla_driven_profiling.md) for optimizing configurations before deployment
 
 ## Notes
 

diff --git a/docs/benchmarks/pre_deployment_profiling.md b/docs/benchmarks/pre_deployment_profiling.md
diff --git a/docs/benchmarks/sla_driven_profiling.md b/docs/benchmarks/sla_driven_profiling.md
diff --git a/docs/images/pd_interpolation.png b/docs/images/pd_interpolation.png
diff --git a/docs/planner/load_planner.md b/docs/planner/load_planner.md
@@ -24,7 +24,7 @@ There are two additional rules set by planner to prevent over-compensation:
 
 ## SLA-based Scaling Up/Down Prefill/Decode Workers
 
-See [Pre-Deployment Profiling](../benchmarks/pre_deployment_profiling.md) for more details.
+See [SLA-Driven Profiling](../benchmarks/sla_driven_profiling.md) for more details.
 
 ## Usage
 

diff --git a/docs/planner/planner_intro.rst b/docs/planner/planner_intro.rst
@@ -78,5 +78,5 @@ Key features include:
 
    Overview <self>
    SLA Planner Quick Start <sla_planner_quickstart>
-   Pre-Deployment Profiling <../benchmarks/pre_deployment_profiling.md>
+   SLA-Driven Profiling <../benchmarks/sla_driven_profiling.md>
    SLA-based Planner <sla_planner.md>
diff --git a/docs/planner/sla_planner.md b/docs/planner/sla_planner.md
@@ -1,7 +1,7 @@
 # SLA-based Planner
 
 > [!TIP]
-> **New to SLA Planner?** For a complete workflow including profiling and deployment, see the [SLA Planner Quick Start Guide](/docs/planner/sla_planner_quickstart.md).
+> **New to SLA Planner?** For a complete workflow including profiling and deployment, see the [SLA Profiling + Planner Quick Start Guide](/docs/planner/sla_planner_quickstart.md).
 
 This document covers information regarding the SLA-based planner in `examples/common/utils/planner_core.py`.
 
@@ -47,11 +47,11 @@ The SLA planner consists of several key components:
 3. **Correction Factors**: Adjust predictions based on observed vs. expected performance
 4. **Scaling Logic**: Calculate optimal number of prefill/decode replicas to meet SLA targets
 
-## Pre-Deployment Profiling
+## SLA-Driven Pre-Deployment Profiling
 
 **Prerequisite**: SLA-based planner requires pre-deployment profiling to be completed before deployment. The profiling process analyzes your model's performance characteristics to determine optimal tensor parallelism configurations and scaling parameters that the planner will use during operation.
 
-See [Pre-Deployment Profiling](../benchmarks/pre_deployment_profiling.md) for detailed instructions on running the profiling process.
+See [Pre-Deployment Profiling](../benchmarks/sla_driven_profiling.md) for detailed instructions on running the profiling process.
 
 ## Load Prediction