Profile Embedding Models with AIPerf | NVIDIA AIPerf Documentation

AIPerf supports benchmarking embedding models that convert text into dense vector representations.

This guide covers profiling OpenAI-compatible embedding endpoints using vLLM.

Section 1. Profile vLLM Embedding Models

Start a vLLM Embedding Server

Launch a vLLM server with an embedding model:

$ docker pull vllm/vllm-openai:latest
$ docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \
>   --model BAAI/bge-small-en-v1.5

Verify the server is ready:

$ curl -s http://localhost:8000/v1/embeddings \
>   -H "Content-Type: application/json" \
>   -d '{"model":"BAAI/bge-small-en-v1.5","input":"test"}' | jq

Profile with Synthetic Inputs

Run AIPerf against the embeddings endpoint using synthetic inputs:

$ aiperf profile \
>     --model BAAI/bge-small-en-v1.5 \
>     --endpoint-type embeddings \
>     --endpoint /v1/embeddings \
>     --synthetic-input-tokens-mean 100 \
>     --synthetic-input-tokens-stddev 0 \
>     --url localhost:8000 \
>     --request-count 20 \
>     --concurrency 4

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     AIPerf System is PROFILING
Profiling: 20/20 |████████████████████████| 100% [00:02<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/BAAI_bge-small-en-v1.5-embeddings-concurrency4/
            NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃                     Metric ┃    avg ┃    min ┃    max ┃    p99 ┃    p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│       Request Latency (ms) │  42.15 │  36.24 │  58.32 │  56.78 │  41.89 │
│  Input Sequence Length (#) │ 100.00 │ 100.00 │ 100.00 │ 100.00 │ 100.00 │
│ Request Throughput (req/s) │   9.52 │      - │      - │      - │      - │
└────────────────────────────┴────────┴────────┴────────┴────────┴────────┘
JSON Export: artifacts/BAAI_bge-small-en-v1.5-embeddings-concurrency4/profile_export_aiperf.json

Embeddings endpoints return metrics focused on request latency and throughput. No token-level metrics (TTFT, ITL) since embeddings return a single vector per request.

Profile with Custom Input File

Create a JSONL embeddings input file:

$ cat <<EOF > inputs.jsonl
$ {"texts": ["What is artificial intelligence?"]}
$ {"texts": ["Explain machine learning."]}
$ {"texts": ["How do neural networks work?"]}
$ {"texts": ["Define deep learning."]}
$ {"texts": ["What are transformers in AI?"]}
$ EOF

Run AIPerf using the custom input file:

$ aiperf profile \
>     --model BAAI/bge-small-en-v1.5 \
>     --endpoint-type embeddings \
>     --endpoint /v1/embeddings \
>     --input-file inputs.jsonl \
>     --custom-dataset-type single_turn \
>     --url localhost:8000 \
>     --request-count 5

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     Loading custom dataset from inputs.jsonl
INFO     AIPerf System is PROFILING
Profiling: 5/5 |████████████████████████| 100% [00:01<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/BAAI_bge-small-en-v1.5-embeddings-custom/
JSON Export: artifacts/BAAI_bge-small-en-v1.5-embeddings-custom/profile_export_aiperf.json

When using custom inputs, AIPerf uses your actual text samples instead of synthetic data. The input sequence lengths will vary based on your actual text content.