Profile Vision Language Models with AIPerf

AIPerf supports benchmarking Vision Language Models (VLMs) that process both text and images.

This guide covers profiling vision models using OpenAI-compatible chat completions endpoints with vLLM.

Start a vLLM Server

Launch a vLLM server with a vision language model:

$ docker pull vllm/vllm-openai:latest
$ docker run --gpus all -p 8000:8000 -e HF_TOKEN vllm/vllm-openai:latest \
>   --model Qwen/Qwen2-VL-2B-Instruct \
>   --enforce-eager

Verify the server is ready:

$ timeout 900 bash -c 'while [ "$(curl -s -o /dev/null -w "%{http_code}" localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d "{\"model\":\"Qwen/Qwen2-VL-2B-Instruct\",\"messages\":[{\"role\":\"user\",\"content\":\"test\"}],\"max_tokens\":1}")" != "200" ]; do sleep 2; done' || { echo "vLLM not ready after 15min"; exit 1; }

Profile with Synthetic Images

AIPerf can generate synthetic images for benchmarking. By default, images are generated as random noise at the requested dimensions — no on-disk assets required, and the pool is effectively unbounded so servers cannot dedupe on identical inputs. Pass --image-source assets to instead sample and resize the 4 bundled natural images (smaller payload bytes), or --image-source <path> to sample from your own directory.

$ aiperf profile \
>     --model Qwen/Qwen2-VL-2B-Instruct \
>     --endpoint-type chat \
>     --image-width-mean 512 \
>     --image-height-mean 512 \
>     --synthetic-input-tokens-mean 100 \
>     --streaming \
>     --url localhost:8000 \
>     --request-count 20 \
>     --concurrency 4

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     Generating synthetic images (512x512 px)
INFO     AIPerf System is PROFILING
Profiling: 20/20 |████████████████████████| 100% [01:45<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency4/
            NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃                      Metric ┃     avg ┃     min ┃     max ┃     p99 ┃     p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│        Request Latency (ms) │ 2345.67 │ 1890.34 │ 2987.12 │ 2923.45 │ 2312.89 │
│    Time to First Token (ms) │  456.78 │  378.90 │  598.45 │  578.23 │  445.67 │
│    Inter Token Latency (ms) │   18.90 │   14.56 │   25.34 │   24.12 │   18.45 │
│ Output Token Count (tokens) │  150.00 │  120.00 │  180.00 │  178.00 │  148.00 │
│  Request Throughput (req/s) │    4.89 │       - │       - │       - │       - │
└─────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┘
JSON Export: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency4/profile_export_aiperf.json

Profile with Custom Input File

Create a JSONL file with text prompts and image URLs:

$ cat <<EOF > inputs.jsonl
$ {"texts": ["Describe this image in detail."], "images": ["https://picsum.photos/512/512?random=1"], "output_length": 200}
$ {"texts": ["What objects are visible in this image?"], "images": ["https://picsum.photos/512/512?random=2"]}
$ {"texts": ["Analyze the composition of this photo."], "images": ["https://picsum.photos/512/512?random=3"], "output_length": 300}
$ {"texts": ["What is the main subject of this image?"], "images": ["https://picsum.photos/512/512?random=4"]}
$ {"texts": ["Provide a caption for this image."], "images": ["https://picsum.photos/512/512?random=5"], "output_length": 50}
$ EOF

Run AIPerf using the custom input file:

$ aiperf profile \
>     --model Qwen/Qwen2-VL-2B-Instruct \
>     --endpoint-type chat \
>     --input-file inputs.jsonl \
>     --custom-dataset-type single_turn \
>     --streaming \
>     --url localhost:8000 \
>     --request-count 5

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     Loaded 5 entries from inputs.jsonl
INFO     Using single_turn dataset type with custom images
INFO     AIPerf System is PROFILING
Profiling: 5/5 |████████████████████████| 100% [00:25<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency1/
            NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃                      Metric ┃     avg ┃     min ┃     max ┃     p99 ┃     p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│        Request Latency (ms) │ 2456.89 │ 2012.45 │ 2890.34 │ 2890.34 │ 2398.12 │
│    Time to First Token (ms) │  478.90 │  398.23 │  567.89 │  567.89 │  467.34 │
│    Inter Token Latency (ms) │   19.45 │   15.67 │   24.12 │   24.12 │   19.01 │
│ Output Token Count (tokens) │  156.00 │  128.00 │  185.00 │  185.00 │  154.00 │
│  Request Throughput (req/s) │    2.34 │       - │       - │       - │       - │
└─────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┘
JSON Export: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency1/profile_export_aiperf.json

AIPerf supports benchmarking Vision Language Models (VLMs) that process both text and images.

This guide covers profiling vision models using OpenAI-compatible chat completions endpoints with vLLM.

Start a vLLM Server

Launch a vLLM server with a vision language model:

$ docker pull vllm/vllm-openai:latest
$ docker run --gpus all -p 8000:8000 -e HF_TOKEN vllm/vllm-openai:latest \
>   --model Qwen/Qwen2-VL-2B-Instruct \
>   --enforce-eager

Verify the server is ready:

$ timeout 900 bash -c 'while [ "$(curl -s -o /dev/null -w "%{http_code}" localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d "{\"model\":\"Qwen/Qwen2-VL-2B-Instruct\",\"messages\":[{\"role\":\"user\",\"content\":\"test\"}],\"max_tokens\":1}")" != "200" ]; do sleep 2; done' || { echo "vLLM not ready after 15min"; exit 1; }

Profile with Synthetic Images

$ aiperf profile \
>     --model Qwen/Qwen2-VL-2B-Instruct \
>     --endpoint-type chat \
>     --image-width-mean 512 \
>     --image-height-mean 512 \
>     --synthetic-input-tokens-mean 100 \
>     --streaming \
>     --url localhost:8000 \
>     --request-count 20 \
>     --concurrency 4

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     Generating synthetic images (512x512 px)
INFO     AIPerf System is PROFILING
Profiling: 20/20 |████████████████████████| 100% [01:45<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency4/
            NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃                      Metric ┃     avg ┃     min ┃     max ┃     p99 ┃     p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│        Request Latency (ms) │ 2345.67 │ 1890.34 │ 2987.12 │ 2923.45 │ 2312.89 │
│    Time to First Token (ms) │  456.78 │  378.90 │  598.45 │  578.23 │  445.67 │
│    Inter Token Latency (ms) │   18.90 │   14.56 │   25.34 │   24.12 │   18.45 │
│ Output Token Count (tokens) │  150.00 │  120.00 │  180.00 │  178.00 │  148.00 │
│  Request Throughput (req/s) │    4.89 │       - │       - │       - │       - │
└─────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┘
JSON Export: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency4/profile_export_aiperf.json

Profile with Custom Input File

Create a JSONL file with text prompts and image URLs:

$ cat <<EOF > inputs.jsonl
$ {"texts": ["Describe this image in detail."], "images": ["https://picsum.photos/512/512?random=1"], "output_length": 200}
$ {"texts": ["What objects are visible in this image?"], "images": ["https://picsum.photos/512/512?random=2"]}
$ {"texts": ["Analyze the composition of this photo."], "images": ["https://picsum.photos/512/512?random=3"], "output_length": 300}
$ {"texts": ["What is the main subject of this image?"], "images": ["https://picsum.photos/512/512?random=4"]}
$ {"texts": ["Provide a caption for this image."], "images": ["https://picsum.photos/512/512?random=5"], "output_length": 50}
$ EOF

Run AIPerf using the custom input file:

$ aiperf profile \
>     --model Qwen/Qwen2-VL-2B-Instruct \
>     --endpoint-type chat \
>     --input-file inputs.jsonl \
>     --custom-dataset-type single_turn \
>     --streaming \
>     --url localhost:8000 \
>     --request-count 5

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     Loaded 5 entries from inputs.jsonl
INFO     Using single_turn dataset type with custom images
INFO     AIPerf System is PROFILING
Profiling: 5/5 |████████████████████████| 100% [00:25<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency1/
            NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃                      Metric ┃     avg ┃     min ┃     max ┃     p99 ┃     p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│        Request Latency (ms) │ 2456.89 │ 2012.45 │ 2890.34 │ 2890.34 │ 2398.12 │
│    Time to First Token (ms) │  478.90 │  398.23 │  567.89 │  567.89 │  467.34 │
│    Inter Token Latency (ms) │   19.45 │   15.67 │   24.12 │   24.12 │   19.01 │
│ Output Token Count (tokens) │  156.00 │  128.00 │  185.00 │  185.00 │  154.00 │
│  Request Throughput (req/s) │    2.34 │       - │       - │       - │       - │
└─────────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┘
JSON Export: artifacts/Qwen_Qwen2-VL-2B-Instruct-chat-concurrency1/profile_export_aiperf.json

$	docker pull vllm/vllm-openai:latest
$	docker run --gpus all -p 8000:8000 -e HF_TOKEN vllm/vllm-openai:latest \
>	--model Qwen/Qwen2-VL-2B-Instruct \
>	--enforce-eager

$	aiperf profile \
>	--model Qwen/Qwen2-VL-2B-Instruct \
>	--endpoint-type chat \
>	--image-width-mean 512 \
>	--image-height-mean 512 \
>	--synthetic-input-tokens-mean 100 \
>	--streaming \
>	--url localhost:8000 \
>	--request-count 20 \
>	--concurrency 4

$	cat <<EOF > inputs.jsonl
$	{"texts": ["Describe this image in detail."], "images": ["https://picsum.photos/512/512?random=1"], "output_length": 200}
$	{"texts": ["What objects are visible in this image?"], "images": ["https://picsum.photos/512/512?random=2"]}
$	{"texts": ["Analyze the composition of this photo."], "images": ["https://picsum.photos/512/512?random=3"], "output_length": 300}
$	{"texts": ["What is the main subject of this image?"], "images": ["https://picsum.photos/512/512?random=4"]}
$	{"texts": ["Provide a caption for this image."], "images": ["https://picsum.photos/512/512?random=5"], "output_length": 50}
$	EOF