Skill

vllm-bench-serve

Name: vllm-bench-serve
Author: vLLM

benchmark LLM serving endpoints

Published by vLLM Updated Jul 18

Covers Performance Benchmarking LLM vLLM API Development

Description

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.

SKILL.md

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):

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:

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

Argument	Default	Description
`--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):

# 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

# 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

Argument	Description
`--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)

--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):

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:

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):

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):

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.

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vllm-bench-serve

Description

SKILL.md

vLLM Bench Serve

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