vllm-bench-serve

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Benchmark vLLM or OpenAI-compatible serving endpoints using vllm bench serve. Supports multiple datasets (random, sharegpt, sonnet, HF), backends (openai, openai-chat, vllm-pooling, embeddings), throughput/latency testing with request-rate control, and result saving. Use when benchmarking LLM serving performance, measuring TTFT/TPOT, or load testing inference APIs.

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NPX Install

npx skill4agent add vllm-project/vllm-skills vllm-bench-serve

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vllm-benchmarkllm-serving-performancethroughput-testinglatency-measurementopenai-compatible-api

SKILL.md Content

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vLLM Bench Serve

Benchmark vLLM or any OpenAI-compatible serving endpoint using the 
vllm bench serve
CLI. Measures throughput, latency (TTFT, TPOT), and goodput against configurable request load.
Reference: vLLM Bench Serve Documentation

Prerequisites

  • vLLM installed (or any OpenAI-compatible server running)
  • A vLLM server or API endpoint already serving a model
  • Python environment with vLLM for the benchmark client

Quick Start

Basic benchmark against local vLLM server (default random dataset, 1000 prompts):
bash
vllm bench serve \
  --backend openai-chat \
  --host 127.0.0.1 \
  --port 8000 \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions
Save results to JSON:
bash
vllm bench serve \
  --backend openai-chat \
  --host 127.0.0.1 \
  --port 8000 \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions \
  --save-result \
  --result-dir ./bench-results \
  --metadata "version=0.6.0" "tp=1"
Note: When using 
--backend openai-chat
, you must specify 
--endpoint /v1/chat/completions
(default is 
/v1/completions
).

Core Arguments

ArgumentDefaultDescription
--backend
openai
Backend type: 
openai
, 
openai-chat
, 
openai-embeddings
, 
vllm
, 
vllm-pooling
, 
vllm-rerank
, etc.
--host
127.0.0.1
Server host
--port
8000
Server port
--base-url
-Alternative: full base URL instead of host:port
--endpoint
/v1/completions
API endpoint; use 
/v1/chat/completions
for openai-chat
--model
(from /v1/models)Model name
--num-prompts
1000
Number of prompts to process
--request-rate
inf
Requests per second; 
inf
= burst all at once
--max-concurrency
-Max concurrent requests (caps parallelism)
--num-warmups
0
Warmup requests before measuring

Datasets

--dataset-name
Use Case
random
Synthetic random prompts (default)
sharegpt
ShareGPT conversation format; requires 
--dataset-path
sonnet
Sonnet-style prompts
hf
HuggingFace dataset; requires 
--dataset-path
(dataset ID)
custom
/ 
custom_mm
Custom dataset; requires 
--dataset-path
prefix_repetition
Prefix repetition benchmark
random-mm
Random multimodal (images/videos)
spec_bench
Spec bench dataset
Dataset-specific options (examples):
bash
# Random: control input/output length
--dataset-name random --random-input-len 1024 --random-output-len 128

# Sonnet defaults: input 550, output 150, prefix 200
--dataset-name sonnet --sonnet-input-len 550 --sonnet-output-len 150

# HuggingFace dataset
--dataset-name hf --dataset-path "lmarena-ai/VisionArena-Chat" --hf-split test

# General overrides (map to dataset-specific args)
--input-len 512 --output-len 256

Load Control

bash
# Fixed request rate (Poisson process)
--request-rate 10

# More bursty arrivals (gamma distribution, burstiness < 1)
--request-rate 10 --burstiness 0.5

# Ramp-up from low to high RPS
--ramp-up-strategy linear --ramp-up-start-rps 1 --ramp-up-end-rps 50

# Limit concurrency (useful for rate-limited APIs)
--max-concurrency 32

Results and Metrics

ArgumentDescription
--save-result
Save benchmark results to JSON
--save-detailed
Include per-request TTFT, TPOT, errors in JSON
--append-result
Append to existing result file
--result-dir
Directory for result files
--result-filename
Custom filename (default: 
{label}-{request_rate}qps-{model}-{timestamp}.json
)
--percentile-metrics
Metrics for percentiles: 
ttft
, 
tpot
, 
itl
, 
e2el
(default: 
ttft,tpot,itl
)
--metric-percentiles
Percentile values, e.g. 
25,50,99
(default: 
99
)
--goodput
SLO for goodput: 
ttft:500 tpot:50
(ms)

Sampling Parameters (OpenAI-compatible backends)

bash
--temperature 0.7 --top-p 0.95 --top-k 50
--frequency-penalty 0 --presence-penalty 0 --repetition-penalty 1.0

Common Workflows

1. Throughput test with random dataset (burst):
bash
vllm bench serve --backend openai-chat --host 127.0.0.1 --port 8000 \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name random \
  --num-prompts 500 --random-input-len 512 --random-output-len 128
2. Latency test with fixed QPS:
bash
vllm bench serve --backend openai-chat --host 127.0.0.1 --port 8000 \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions \
  --request-rate 5 --num-prompts 200 \
  --save-result --percentile-metrics ttft,tpot --metric-percentiles 50,99
3. Benchmark against remote API (base-url):
bash
vllm bench serve --backend openai-chat \
  --base-url "https://api.example.com/v1" \
  --model my-model \
  --header "Authorization=Bearer $API_KEY"
4. Run inside Docker (when vLLM client not on host):
bash
docker exec <container-name> vllm bench serve \
  --backend openai-chat --host 127.0.0.1 --port 8000 \
  --model Qwen/Qwen2.5-1.5B-Instruct \
  --endpoint /v1/chat/completions \
  --dataset-name random --num-prompts 100

Troubleshooting

  • Connection refused: Ensure the server is running and 
    --host
    /
    --port
    or 
    --base-url
    are correct.
  • Model not found: Pass 
    --model
    explicitly or ensure 
    /v1/models
    returns the model.
  • URL must end with chat/completions: Use 
    --endpoint /v1/chat/completions
    when 
    --backend openai-chat
    .
  • Rate limit / 429: Reduce 
    --request-rate
    or 
    --max-concurrency
    .
  • Ready check: Use 
    --ready-check-timeout-sec 60
    to wait for the endpoint before benchmarking.
  • SSL: Use 
    --insecure
    for self-signed certificates.

Notes

  • For embeddings/rerank benchmarks, use 
    --backend openai-embeddings
    , 
    vllm-pooling
    , or 
    vllm-rerank
    .
  • --profile
    requires 
    --profiler-config
    on the server for vLLM profiling.
  • Goodput SLOs are useful for SLA-style analysis; see DistServe paper for details.