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Profile NIM Image Retrieval with AIPerf

AIPerf supports benchmarking NVIDIA NIM image retrieval endpoints that detect objects and layout elements (tables, charts, titles, etc.) in images and return bounding box coordinates.

This guide covers profiling NIM for Object Detection models such as nemoretriever-page-elements and nemoretriever-graphic-elements using the /v1/infer API.

Section 1. Deploy the NIM Server

Prerequisites

  • NVIDIA GPU (Ampere, Hopper, or Lovelace architecture)
  • Docker with NVIDIA runtime
  • An NGC API key

Authenticate with NGC

export NGC_API_KEY=<your_api_key>
echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

Start the NIM Container

Launch the NIM for Object Detection (page elements):

export NIM_MODEL_NAME=nvidia/nemoretriever-page-elements-v3
export CONTAINER_NAME=$(basename $NIM_MODEL_NAME)
export IMG_NAME="nvcr.io/nim/nvidia/$CONTAINER_NAME:1.7.0"
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"

docker run -it --rm --name=$CONTAINER_NAME \
  --runtime=nvidia \
  --gpus all \
  --shm-size=16GB \
  -e NGC_API_KEY \
  -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
  -u $(id -u) \
  -p 8000:8000 \
  $IMG_NAME

Wait for the server to start, then verify it is ready:

curl -s http://localhost:8000/v1/health/ready

Verify with a Test Request

IMAGE_URL="https://assets.ngc.nvidia.com/products/api-catalog/nemo-retriever/object-detection/page-elements-example-1.jpg"
BASE64_IMAGE=$(curl -s "$IMAGE_URL" | base64 -w 0)

curl -s -X POST http://localhost:8000/v1/infer \
  -H 'Content-Type: application/json' \
  -d '{
    "input": [{
      "type": "image_url",
      "url": "data:image/jpeg;base64,'"$BASE64_IMAGE"'"
    }]
  }' | jq

Sample Response:

{
  "data": [
    {
      "index": 0,
      "bounding_boxes": {
        "title": [
          { "x_min": 0.12, "y_min": 0.02, "x_max": 0.88, "y_max": 0.06, "confidence": 0.97 }
        ],
        "table": [
          { "x_min": 0.05, "y_min": 0.15, "x_max": 0.95, "y_max": 0.85, "confidence": 0.94 }
        ]
      }
    }
  ],
  "usage": { "images_size_mb": 0.42 }
}

Bounding box coordinates are normalized (0-1 range) relative to the top-left corner of the image.

Section 2. Profile with Custom Images

Create a JSONL input file with image paths or URLs:

cat <<EOF > inputs.jsonl
{"image": "https://assets.ngc.nvidia.com/products/api-catalog/nemo-retriever/object-detection/page-elements-example-1.jpg"}
{"image": "/path/to/local/document_page.png"}
{"image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUg..."}
EOF

Each line should contain an image field with either:

  • A URL to a remote image
  • A local file path (automatically encoded to base64)
  • A base64 data URL (passed through as-is)

Run AIPerf against the image retrieval endpoint:

aiperf profile \
    --endpoint-type image_retrieval \
    --model nvidia/nemoretriever-page-elements-v3 \
    --url localhost:8000 \
    --input-file inputs.jsonl \
    --custom-dataset-type single_turn \
    --request-count 20 \
    --concurrency 4

Sample Output:

INFO     Starting AIPerf System
INFO     Tokenization is disabled for this endpoint, skipping tokenizer configuration
INFO     AIPerf System is PROFILING

Profiling: 20/20 |████████████████████████| 100% [00:05<00:00]

INFO     Benchmark completed successfully

                  NVIDIA AIPerf | Image Retrieval Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃                           Metric ┃    avg ┃    min ┃    max ┃    p99 ┃    p90 ┃    p50 ┃    std ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│             Request Latency (ms) │  85.23 │  62.10 │ 142.56 │ 138.90 │ 125.30 │  82.45 │  21.34 │
│  Image Throughput (images/sec)   │  11.73 │   7.01 │  16.10 │   7.20 │   8.05 │  12.13 │   2.45 │
│       Image Latency (ms/image)   │  85.23 │  62.10 │ 142.56 │ 138.90 │ 125.30 │  82.45 │  21.34 │
│  Request Throughput (requests/sec) │  11.73 │    N/A │    N/A │    N/A │    N/A │    N/A │    N/A │
└──────────────────────────────────┴────────┴────────┴────────┴────────┴────────┴────────┴────────┘

Since this endpoint does not produce tokens, no TTFT or ITL metrics are reported. The primary metrics are request latency, image throughput, and image latency.

Section 3. Profile with Multiple Images per Request

You can send multiple images in a single request:

cat <<EOF > multi_image_inputs.jsonl
{"images": ["https://example.com/page1.png", "https://example.com/page2.png"]}
{"images": ["/path/to/chart1.jpg", "/path/to/chart2.jpg", "/path/to/chart3.jpg"]}
EOF
aiperf profile \
    --endpoint-type image_retrieval \
    --model nvidia/nemoretriever-page-elements-v3 \
    --url localhost:8000 \
    --input-file multi_image_inputs.jsonl \
    --custom-dataset-type single_turn \
    --request-count 10 \
    --concurrency 2

When sending multiple images per request, the image throughput metric reflects the total number of images processed per second across all requests.

Section 4. Pass Extra Parameters

Use --extra-inputs to pass additional parameters to the NIM endpoint:

aiperf profile \
    --endpoint-type image_retrieval \
    --model nvidia/nemoretriever-page-elements-v3 \
    --url localhost:8000 \
    --input-file inputs.jsonl \
    --custom-dataset-type single_turn \
    --extra-inputs threshold:0.5 \
    --request-count 20 \
    --concurrency 4

Extra inputs are merged into the request payload alongside the image data.

Section 5. Using Other NIM Object Detection Models

The image_retrieval endpoint works with any NIM that accepts the /v1/infer API format. You can swap models by changing the Docker image:

Model Container Image Description
Page Elements v3 nvcr.io/nim/nvidia/nemoretriever-page-elements-v3:1.7.0 Detects tables, charts, titles, paragraphs, headers/footers
Graphic Elements v1 nvcr.io/nim/nvidia/nemoretriever-graphic-elements-v1:latest Detects components within charts (axes, legends, data points)
Table Structure v1 nvcr.io/nim/nvidia/nemoretriever-table-structure-v1:latest Identifies table cells, rows, and columns

All models share the same /v1/infer request/response format, so the same AIPerf --endpoint-type image_retrieval command works for each.