---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
---

# SLA-Driven Profiling with DynamoGraphDeploymentRequest

> [!TIP]
> **New to DGDR and SLA-Driven Profiling?** Start with the [SLA-Driven Profiling and Planner Deployment Quick Start Guide](../planner/sla-planner-quickstart.md) for step-by-step instructions. This document provides deeper technical details about the profiling process.

## Overview

Dynamo provides automated SLA-driven profiling through **DynamoGraphDeploymentRequests (DGDR)**. Instead of manually running profiling scripts, you declare your performance requirements and let the Dynamo Operator handle profiling and deployment automatically.

**Key Benefits:**
- **Declarative**: Specify SLAs, not implementation details
- **Automated**: No manual job setup or result processing
- **Integrated**: Seamlessly works with Dynamo Operator
- **Production-Ready**: Generates optimized configurations with SLA planner

This document covers:
- Technical details of online vs offline profiling
- Profiling process internals (GPU usage, measurements, interpolation)
- Direct script usage for advanced scenarios
- Comprehensive troubleshooting

## Support Matrix

| Backend | Dense Models | MoE Models |
|---------|-------------|------------|
| vLLM | ✅ | 🚧 |
| SGLang | ✅ | ✅ |
| TensorRT-LLM | ✅ | 🚧 |

Specifically, the profiler sweeps over the following parallelization mapping for prefill and decode:
| Model Architecture | Prefill Parallelization Mapping | Decode Parallelization Mapping |
|---------|-------------|------------|
| MLA+MoE (DeepseekV3ForCausalLM, DeepseekV32ForCausalLM) | TEP, DEP | TEP, DEP |
| GQA+MoE (Qwen3MoeForCausalLM) | TP, TEP, DEP | TP, TEP, DEP |
| Other Models | TP | TP |

> [!NOTE]
> - Exact model x parallelization mapping support is dependent on the backend. The profiler does not guarantee that the recommended P/D engine configuration is supported and bug-free by the backend.

## Using DGDR for Profiling (Recommended)

The recommended way to profile models is through DGDRs. Sample configurations are provided in `deploy/`:

**Available Samples:**
- **`profile_sla_dgdr.yaml`**: Standard profiling with AIPerf on real engines
- **`profile_sla_aic_dgdr.yaml`**: Fast profiling with AI Configurator simulation
- **`profile_sla_moe_dgdr.yaml`**: MoE model profiling

The Dynamo Operator automatically:
1. Discovers GPU resources (cluster-scoped operators only)
2. Runs profiling (AIPerf on real engines or AI Configurator simulation)
3. Generates optimal DGD configuration with SLA planner
4. Deploys the DGD to your cluster

See the [Quick Start Guide](../planner/sla-planner-quickstart.md) for prerequisites and detailed instructions.

## Hardware Configuration

Hardware parameters have sensible defaults and are **optional** - you can override them if needed:

```yaml
profilingConfig:
  config:
    # Override hardware defaults if needed
    hardware:
      min_num_gpus_per_engine: 1
      max_num_gpus_per_engine: 8
      num_gpus_per_node: 8

    # Only needed when using AI Configurator (sweep.use_ai_configurator: true)
    sweep:
      aic_system: h200_sxm  # GPU type for AI Configurator (h100_sxm, h200_sxm, etc.)
```

### Automatic GPU Discovery (Optional Feature)

Cluster-scoped operators can optionally enable automatic GPU discovery to detect hardware from cluster nodes. When enabled, hardware config is auto-detected and overrides any manually specified values.

```yaml
spec:
  enableGpuDiscovery: true
```

This feature is only available with cluster-scoped operators (`namespaceRestriction.enabled=false`) as it requires cluster-wide node access permissions. It is not available for namespace-restricted operators.

## Profiling Method

1. **Hardware Setup**: Uses defaults or user-specified hardware configuration. Optionally, cluster-scoped operators can enable automatic GPU discovery to detect specifications from cluster nodes.
2. **Identify Sweep Ranges**: Automatically determine minimum and maximum number of GPUs per engine. Minimum is determined by the model size and GPU VRAM. Maximum is set to one node for dense model and 4 nodes for MoE models.
3. **Parallelization Mapping Sweep**: Use the input ISL and OSL, test the performance of the engines with different parallelization mappings.
   - For dense models, we test different TP sizes for both prefill and decode.
   - For MoE models (SGLang), we evaluate both TEP and DEP as candidates for prefill and decode.
   - **Prefill**:
     - TP/TEP: We measure TTFT with batch size = 1 (assuming ISL is long enough to saturate compute) without KV reuse.
     - DEP: Attention uses data parallelism. We send a single burst with total concurrency `attention_dp_size × attn_dp_num_req_ratio` (defaults to 4) and compute the reported TTFT as `time_to_first_token.max / attn_dp_num_req_ratio` from the AIPerf summary of that burst. This stabilizes measurements when the first batch may launch before all requests arrive.
   ![Prefill Performance](../../assets/img/h100-prefill-performance.png)
   - **Decode**: Since the ITL (or iteration time) is relevant with how many requests are in-flight, we measure the ITL under different number of in-flight requests. The range of the number of in-flight requests is from 1 to the maximum number of requests that the kv cache of the engine can hold. To measure the ITL without being affected by piggy-backed prefill requests, the script will enable kv-reuse and warm up the engine by issuing the same prompts before measuring the ITL. Since the kv cache is sufficient for all the requests, it can hold the kv cache of the pre-computed prompts and skip the prefill phase when measuring the ITL. However, for MoE models, this is not guaranteed because the kv cache in different attention DP ranks is different. We are working on framework-side change to fix this issue. For example, the below plot shows the decode parallelization mapping sweep results for H100 for deepseek-ai/DeepSeek-R1-Distill-Llama-8B.
   ![Decode Performance](../../assets/img/h100-decode-performance.png)
4. **Recommendation**: Selects optimal parallelization mapping for prefill and decode that achieves the highest per GPU throughput while adhering the SLA on TTFT and ITL. Specifically, the profiler will choose the point (or a point on the curve for decode) that is left to the vertical red dashed line that represents the SLAs while has the highest y coordinate (throughput per GPU).
5. **In-Depth Profiling on the Recommended P/D Engine**: After finding the best TP size for prefill and decode, the script will then interpolate the TTFT with ISL and ITL with active KV cache and decode context length. This is to provide a more accurate estimation of the performance when ISL and OSL changes and will be used in the sla-planner.
![ITL Interpolation](../../assets/img/pd-interpolation.png)
   - **Prefill**: Measures TTFT and throughput per GPU across different input lengths with batch size=1.
   - **Decode**: Measures ITL and throughput per GPU under various KV cache loads and decode context lengths. The active kv usage determines the complexity of the memory-bounded attention kernel while the active kv usage divided the average context length determines the complexity of the computation bound MLP kernel. For example, the below figure shows the ITL of DS-Distilled Llama 8b model on H100 TP4. The ITL grows near-linearly with active kv usage under a fixed context length. And the slope increases as the context length decreases.


To run the parallelization mapping sweep and the in-depth profiling on the recommended P/D engine, the profiler need to know the engine's forward pass time with different loads. There are two ways to achieve this: run AIPerf on real engines or use AI Configurator to run simulations.

### AIPerf on Real Engines

Profiles your model by creating real test deployments in Kubernetes and measuring their performance.

**Characteristics:**
- **Duration**: 2-4 hours
- **Accuracy**: Highest (real measurements)
- **GPU Requirements**: Full access to test different parallelization mappings
- **Backends**: vLLM, SGLang, TensorRT-LLM

**DGDR Configuration:**
```yaml
profilingConfig:
  config:
    sweep:
      use_ai_configurator: false  # Default
```

### AI Configurator Simulation

Uses performance simulation to rapidly estimate optimal configurations without running real deployments.

**Characteristics:**
- **Duration**: 20-30 seconds
- **Accuracy**: Estimated (may have errors for unusual configurations)
- **GPU Requirements**: None
- **Backends**: TensorRT-LLM only (vLLM/SGLang coming soon)

**DGDR Configuration:**
```yaml
profilingConfig:
  config:
    sweep:
      use_ai_configurator: true
    aic:
      system: h200_sxm          # GPU system type
      model_name: QWEN3_32B     # AIC model identifier
      backend_version: "0.20.0"
```

**Supported Configurations:**

For the current list of supported models, systems, and backend versions, see the [AI Configurator documentation](https://github.com/ai-dynamo/aiconfigurator#supported-features).

To check from the command line: `aiconfigurator cli --help`

**Currently supports:**
- **Backends**: TensorRT-LLM (versions 0.20.0, 1.0.0rc3, 1.0.0rc6)
- **Systems**: H100 SXM, H200 SXM, B200 SXM, GB200 SXM, A100 SXM
- **Models**: Wide range including GPT, Llama, Mixtral, DeepSeek, Qwen, and more

### Output Format

After profiling, the DGDR status contains:

1. **Recommended Configuration**: Optimal TP for prefill and decode
2. **Performance Data**: Interpolation models for SLA planner
3. **Generated DGD**: Complete deployment manifest

**Example Recommendations:**
```
Suggested prefill TP:4 (TTFT 48.37 ms, throughput 15505.23 tokens/s/GPU)
Suggested decode TP:4 (ITL 4.83 ms, throughput 51.22 tokens/s/GPU)
```

#### Interactive Configuration Selection WebUI

When running the profiler with `--pick-with-webui`, an interactive web interface is launched that allows you to visually explore profiling results and manually select configurations.

**Features:**
- **Interactive Charts**: Visualize prefill TTFT, decode ITL, and GPU hours analysis with hover-to-highlight synchronization between charts and tables
- **Pareto-Optimal Analysis**: The GPU Hours table shows pareto-optimal configurations balancing latency and throughput
- **DGD Config Preview**: Click "Show Config" on any row to view the corresponding DynamoGraphDeployment YAML
- **GPU Cost Estimation**: Toggle GPU cost display to convert GPU hours to cost ($/1000 requests)
- **SLA Visualization**: Red dashed lines indicate your TTFT and ITL targets

**Selection Methods:**
1. **GPU Hours Table** (recommended): Click any row to select both prefill and decode configurations at once based on the pareto-optimal combination
2. **Individual Selection**: Click one row in the Prefill table AND one row in the Decode table to manually choose each

**Example DGD Config Output:**

When you click "Show Config", you'll see a DynamoGraphDeployment configuration like:

```yaml
# DynamoGraphDeployment Configuration
# Prefill: 1 GPU(s), TP=1
# Decode: 4 GPU(s), TP=4
# Model: Qwen/Qwen3-32B-FP8
# Backend: trtllm
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
spec:
  services:
    PrefillWorker:
      subComponentType: prefill
      replicas: 1
      extraPodSpec:
        mainContainer:
          args:
          - --tensor-parallel-size=1
    DecodeWorker:
      subComponentType: decode
      replicas: 1
      extraPodSpec:
        mainContainer:
          args:
          - --tensor-parallel-size=4
```

**Usage:**
```bash
python -m benchmarks.profiler.profile_sla \
  --backend trtllm \
  --config path/to/disagg.yaml \
  --pick-with-webui \
  --use-ai-configurator \
  --model Qwen/Qwen3-32B-FP8 \
  --aic-system h200_sxm \
  --ttft 200 --itl 15
```

Once you have selected a configuration, the full DynamoGraphDeployment CRD will be saved in your output folder as `config_with_planner.yaml`.

The WebUI launches on port 8000 by default (configurable with `--webui-port`).

#### Output Performance Plots

The profiler will generate the following plots to better visualize the performance data:

**Parallelization Mapping Sweep Plots:**
- `prefill_performance.png`: TTFT vs Parallelization Mapping size
- `decode_performance.png`: ITL vs Parallelization Mapping size and in-flight requests

Note these two plots are based on the input ISL and OSL.

**In-Depth Profiling for the Recommended P/D Engine Plots:**
- `selected_prefill_interpolation/prefill_ttft_interpolation.png`: TTFT vs ISL for the recommended prefill engine
- `selected_prefill_interpolation/prefill_throughput_interpolation.png`: Throughput vs ISL for the recommended prefill engine
- `selected_decode_interpolation/decode_itl_interplation.png`: ITL vs KV usage and context length for the recommended decode engine
- `selected_decode_interpolation/decode_throughput_interpolation.png`: Throughput vs KV usage and context length for the recommended decode engine


### Output Interpolation Data

The profiler generates `.npz` files to store the performance data for the recommended P/D engine:

**Prefill Interpolation** (`selected_prefill_interpolation/raw_data.npz`):
- `prefill_isl`: 1D array of input sequence lengths tested
- `prefill_ttft`: 1D array of TTFTs (ms) at each ISL
- `prefill_thpt_per_gpu`: 1D array of throughput (tokens/s/GPU) at each ISL

**Decode Interpolation** (`selected_decode_interpolation/raw_data.npz`):
- `max_kv_tokens`: Total KV tokens capacity in decode engine
- `x_kv_usage`: 1D array of active KV usage percentages [0, 1]
- `y_context_length`: 1D array of average context lengths tested
- `z_itl`: 1D array of ITLs (ms) at each (KV usage, context length) point
- `z_thpt_per_gpu`: 1D array of throughput (tokens/s/GPU) at each point

## DGDR Configuration Reference

This section provides detailed explanations of all DGDR `profilingConfig` options. The DGDR controller passes this configuration to the profiler script, which is defined in `benchmarks/profiler/utils/profiler_argparse.py`.

### Configuration Structure

All profiler configuration goes under `spec.profilingConfig.config`:

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeploymentRequest
metadata:
  name: my-deployment
spec:
  model: "Qwen/Qwen3-0.6B"         # High-level: model to deploy
  backend: vllm                    # High-level: inference backend

  profilingConfig:
    profilerImage: "nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.1"  # Required
    configMapRef:                  # Optional: base DGD config
      name: my-config
      key: disagg.yaml

    config:                        # Profiler configuration
      sla: { ... }
      hardware: { ... }
      sweep: { ... }
      aic: { ... }
      planner: { ... }

  deploymentOverrides:             # Optional
    workersImage: "nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.1"
```

### SLA Configuration (Required)

Define your performance requirements and workload characteristics:

```yaml
profilingConfig:
  config:
    sla:
      isl: 3000      # Average input sequence length (tokens)
      osl: 150       # Average output sequence length (tokens)
      ttft: 200.0    # Target Time To First Token (milliseconds)
      itl: 20.0      # Target Inter-Token Latency (milliseconds)
```

**What these control:**
- **ISL/OSL**: Based on your expected traffic patterns
- **TTFT**: First token latency target (lower = more GPUs needed, affects prefill engine)
- **ITL**: Token generation latency target (lower = more GPUs needed, affects decode engine)
- **Trade-offs**: Tighter SLAs require more GPU resources

### Hardware Configuration (Optional)

Control GPU search space and constraints:

```yaml
profilingConfig:
  config:
    hardware:
      min_num_gpus_per_engine: 2      # if not provided, will automatically determine based on model and VRAM size
      max_num_gpus_per_engine: 8      # Maximum GPUs to test
      num_gpus_per_node: 8            # GPUs per node (for multi-node MoE)
      gpu_type: h200_sxm              # GPU type hint
```

**When to use:**
- **min_num_gpus_per_engine**: Skip small TP sizes if your model is large
- **max_num_gpus_per_engine**: Limit search space or work around constraints (e.g., [AIC attention heads](#ai-configurator-attention-head-constraint-error))
- **num_gpus_per_node**: Determine the upper bound of number of GPUs per node for dense models and configure Grove for multi-node MoE engines.
- **gpu_type**: Informational, auto-detected by controller

> [!TIP]
> If you don't specify hardware constraints, the controller auto-detects based on your model size and available cluster resources.

### Sweep Configuration (Optional)

Control profiling behavior:

```yaml
profilingConfig:
  config:
    sweep:
      use_ai_configurator: false              # Use offline profiling (default: false)
      prefill_interpolation_granularity: 16   # Samples for prefill TTFT curve
      decode_interpolation_granularity: 6     # Samples for decode ITL curve
```

**Use cases:**
- **use_ai_configurator**: Set to `true` for 20-30 second profiling (TensorRT-LLM only)
- **prefill_interpolation_granularity**: How many samples to benchmark for prefill TTFT curve (lower = faster but may be less accurate)
- **decode_interpolation_granularity**: How many samples to benchmark for decode ITL curve (lower = faster but may be less accurate). Since ITL interpolation is a 3d plot and takes longer to run, we default to a smaller number of samples. Increasing this value might quadratically increase the profiling time.

### AI Configurator Configuration (Required if `use_ai_configurator: true`)

Configure AI Configurator profiling mode:

```yaml
profilingConfig:
  config:
    sweep:
      use_ai_configurator: true
      aic_system: h200_sxm              # GPU system: h100_sxm, h200_sxm, b200_sxm, gb200_sxm, a100_sxm
      aic_hf_id: Qwen/Qwen3-32B         # Huggingface model id
      aic_backend_version: "0.20.0"     # TensorRT-LLM version: 0.20.0, 1.0.0rc3
```

**Supported configurations:** See [AI Configurator documentation](https://github.com/ai-dynamo/aiconfigurator#supported-features)

### Planner Configuration (Optional)

Pass arguments to the SLA planner:

```yaml
profilingConfig:
  config:
    planner:
      planner_min_endpoint: 2                    # Minimum endpoints to maintain
      planner_adjustment_interval: 60            # Adjustment interval (seconds)
      planner_load_predictor: linear             # Load prediction method
```

> [!NOTE]
> Planner arguments use `planner_` prefix. See planner documentation for full list.

### Engine Configuration (Auto-configured)

The controller automatically sets these from high-level fields:

```yaml
# You specify:
spec:
  model: "Qwen/Qwen3-0.6B"
  backend: vllm

# Controller auto-injects into config:
profilingConfig:
  config:
    deployment:
      model: "Qwen/Qwen3-0.6B"       # From spec.model
    engine:
      backend: vllm                  # From spec.backend
      config: /path/to/configmap     # From spec.profilingConfig.configMapRef (if provided)
```

**You should not manually set** `deployment.model` or `engine.backend` in `profilingConfig.config` - they are automatically injected from the high-level fields.

### Complete Example: AIPerf on Real Engines

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeploymentRequest
metadata:
  name: vllm-dense-online
spec:
  model: "Qwen/Qwen3-0.6B"
  backend: vllm

  profilingConfig:
    profilerImage: "nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.1"
    config:
      sla:
        isl: 3000
        osl: 150
        ttft: 200.0
        itl: 20.0

      hardware:
        min_num_gpus_per_engine: 1
        max_num_gpus_per_engine: 8

      sweep:
        use_ai_configurator: false

  deploymentOverrides:
    workersImage: "nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.1"

  autoApply: true
```

### Complete Example: AI Configurator Simulation

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeploymentRequest
metadata:
  name: trtllm-aic-offline
spec:
  model: "Qwen/Qwen3-32B"
  backend: trtllm

  profilingConfig:
    profilerImage: "nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:0.6.1"
    config:
      sla:
        isl: 4000
        osl: 500
        ttft: 300.0
        itl: 10.0

      sweep:
        use_ai_configurator: true

      aic:
        system: h200_sxm
        model_name: QWEN3_32B
        backend_version: "0.20.0"

  deploymentOverrides:
    workersImage: "nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:0.6.1"

  autoApply: true
```

### Complete Example: MoE Model

```yaml
apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeploymentRequest
metadata:
  name: sglang-moe
spec:
  model: "deepseek-ai/DeepSeek-R1"
  backend: sglang

  profilingConfig:
    profilerImage: "nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.6.1"
    config:
      sla:
        isl: 2048
        osl: 512
        ttft: 300.0
        itl: 25.0

      hardware:
        num_gpus_per_node: 8
        max_num_gpus_per_engine: 32

      engine:
        is_moe_model: true       # Enable MoE profiling mode

  deploymentOverrides:
    workersImage: "nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.6.1"

  autoApply: true
```

## Troubleshooting

### Profiling Takes Too Long

**Solution 1**: Use AI Configurator for rapid profiling (TensorRT-LLM only):
```yaml
sweep:
  use_ai_configurator: true
```

**Solution 2**: Reduce search space:
```yaml
config:
  sweep:
    min_num_gpus: 4  # Skip TP1, TP2
    max_num_gpus: 8  # Don't test beyond TP8
```

### SLA Cannot Be Met

**Symptoms**: Profiler reports no configuration meets targets

**Solutions:**
1. Relax SLA targets (increase TTFT/ITL)
2. Add more GPU resources
3. Try a different backend
4. Use a smaller model

### AI Configurator: Attention Head Constraint Error

**Symptoms**: Profiling fails with error:
```
AssertionError: num_heads <N> should be divisible by tp_size <M> and the division result should be >= 4
```

**Cause**: AI Configurator requires **≥4 attention heads per GPU**. Small models with few heads cannot use high TP sizes.

**Affected Models:**
- **Qwen3-0.6B** (16 heads): Max TP = 4 ❌ Fails at TP=8
- **GPT-2** (12 heads): Max TP = 3
- Most models **\<1B parameters**: May hit this constraint

**Solution**: Limit `max_num_gpus_per_engine` in your DGDR:

```yaml
profilingConfig:
  profilerImage: "nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:0.6.1"
  config:
    hardware:
      max_num_gpus_per_engine: 4  # For Qwen3-0.6B (16 heads / 4 = max TP of 4)
    sweep:
      use_ai_configurator: true
    aic:
      system: h200_sxm
      model_name: QWEN3_0_6B
```

**Calculate Max TP**: `max_tp = num_attention_heads / 4`

> **Note**: This is an AI Configurator limitation. Online profiling doesn't have this constraint.

### Image Pull Errors

**Symptoms**: `ErrImagePull` or `ImagePullBackOff`

**Solution**: Ensure image pull secrets are configured:
```bash
kubectl create secret docker-registry nvcr-imagepullsecret \
  --docker-server=nvcr.io \
  --docker-username='$oauthtoken' \
  --docker-password=<NGC_API_KEY> \
  --namespace <your-namespace>
```

### Out of Memory During Profiling

**Symptoms**: OOM errors in profiling jobs

**Solutions:**
1. Reduce `gpu_memory_utilization` in engine config
2. Reduce `--max-context-length`
3. Skip larger TP configurations
4. Use fewer GPUs per test

### Unsupported Parallelization Mapping in Backend

**Symptoms**: Starttime/runtime error in the backend. For example, prime number of attention heads restrain TP size to be 1 (i.e., falcon-7b with 71 attention heads). Or some backend does not support different TP sizes for prefill and decode.

**Solutions:**
1. Contact the backend to add support for the use cases and bump backend version in dynamo.
2. Restrain the max and min number of GPUs per engine to the supported range.

## Next Steps

- **Deploy with DGDR**: See [Quick Start Guide](../planner/sla-planner-quickstart.md)
- **Understand SLA Planner**: Read [SLA Planner Deep Dive](../planner/sla-planner.md)
- **Monitor Deployments**: Set up [Observability](../kubernetes/observability/metrics.md)
- **Optimize Performance**: See [Performance Tuning](../performance/tuning.md)

## Related Documentation

- [DGDR API Reference](../kubernetes/api-reference.md)
- [SLA Planner Quick Start](../planner/sla-planner-quickstart.md)
- [SLA Planner Architecture](../planner/sla-planner.md)
- [Profiler Arguments Reference](https://github.com/ai-dynamo/dynamo/tree/main/benchmarks/profiler/utils/profiler_argparse.py)
