Vllm Bench Random Synthetic

vLLM Benchmark with Random Synthetic Data

Run a quick performance benchmark on a vLLM server using synthetic random data. This skill measures core serving metrics including request throughput, token throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and inter-token latency.

When to use

• User wants to quickly benchmark vLLM serving performance
• User wants to measure throughput and latency metrics without downloading datasets
• User wants to test a vLLM deployment with synthetic workload
• User wants baseline performance numbers for a specific model

Prerequisites

• vLLM must be installed (pip install vllm)
• A vLLM server must be running (or can be started as part of the benchmark)
• For GPU models, NVIDIA GPU with appropriate drivers must be available

Quick Start

The simplest way to run the benchmark:

# Start vLLM server (in background or separate terminal)
vllm serve Qwen/Qwen2.5-1.5B-Instruct

# Run benchmark with random synthetic data
vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name random \
  --num-prompts 10

Note:

• Use --backend openai-chat with endpoint /v1/chat/completions for online benchmarks.

Parameters

| Parameter | Description | Default | |-----------|-------------|---------| | --backend | Backend type: vllm, openai, openai-chat | vllm | | --model | Model name (must match the server) | Required | | --endpoint | API endpoint path | /v1/completions or /v1/chat/completions | | --dataset-name | Dataset to use | random (synthetic) | | --num-prompts | Number of requests to send | 10 | | --port | Server port | 8000 | | --max-concurrency | Maximum concurrent requests | Auto | | --save-result | Save results to file | Off | | --result-dir | Directory to save results | ./ |

Expected Output

When successful, you will see output like:

============ Serving Benchmark Result ============
Successful requests:                     10
Benchmark duration (s):                  5.78
Total input tokens:                      1369
Total generated tokens:                  2212
Request throughput (req/s):              1.73
Output token throughput (tok/s):         382.89
Total token throughput (tok/s):          619.85
---------------Time to First Token----------------
Mean TTFT (ms):                          71.54
Median TTFT (ms):                        73.88
P99 TTFT (ms):                           79.49
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          7.91
Median TPOT (ms):                        7.96
P99 TPOT (ms):                           8.03
---------------Inter-token Latency----------------
Mean ITL (ms):                           7.74
Median ITL (ms):                         7.70
P99 ITL (ms):                            8.39
==================================================

Advanced Usage

With more prompts for better statistics

vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name random \
  --num-prompts 100

Save results to file

vllm bench serve \
  --backend openai-chat \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name random \
  --num-prompts 50 \
  --save-result \
  --result-dir ./benchmark-results/

Custom port and concurrency

vllm bench serve \
  --backend openai-chat \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name random \
  --num-prompts 100 \
  --port 8001 \
  --max-concurrency 4

Model Recommendations

For quick testing (small models, fast):

• Qwen/Qwen2.5-1.5B-Instruct (recommended for quick tests)
• facebook/opt-125m
• facebook/opt-350m

For realistic benchmarks (medium models):

• Qwen/Qwen2.5-7B-Instruct
• meta-llama/Llama-3.1-8B-Instruct
• mistralai/Mistral-7B-Instruct-v0.3

Workflow

1. Check if vLLM is installed: Run vllm --version to verify
2. Check if server is already running: Run curl http://localhost:8000/health to check
3. Start vLLM server if needed: Run vllm serve (wait for "Application startup complete")
4. Run benchmark: Execute vllm bench serve with appropriate parameters
5. Review results: Check throughput and latency metrics
6. Clean up: If the agent skill started the vLLM server (not a pre-existing one), stop it after benchmark completion using kill

Troubleshooting

Server not responding:

• Check if server is running: curl http://localhost:8000/health
• Verify port matches: Use --port flag if server is on different port

Model not found:

• Ensure model name matches exactly between server and benchmark
• Check HuggingFace access: export HF_TOKEN= if needed

Out of memory:

• Use a smaller model (e.g., Qwen2.5-1.5B-Instruct)
• Reduce --num-prompts or --max-concurrency

Connection refused:

• Server may still be starting (wait for "Application startup complete")
• Check firewall or network settings

Notes

• The random dataset generates synthetic prompts automatically
• Benchmark duration scales with --num-prompts
• For production benchmarking, use at least 100 prompts for stable statistics
• Results may vary based on hardware, model size, and system load
• First run may be slower due to model loading and compilation
• Important: If the agent skill starts a vLLM server for benchmarking, it must stop the server after the benchmark completes to free up resources. Do not stop pre-existing servers that were already running before the benchmark.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: vllm-project
• Source: vllm-project/vllm-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.