vllm/docs/cli

vLLM CLI Guide

The vllm command-line tool is used to run and manage vLLM models. You can start by viewing the help message with:

vllm --help

Available Commands:

vllm {chat,complete,serve,bench,collect-env,run-batch}

Table of Contents

• serve
• chat
• complete
• bench
  • latency
  • serve
  • throughput
• collect-env
• run-batch
• More Help

serve

Start the vLLM OpenAI Compatible API server.

Examples:

# Start with a model
vllm serve meta-llama/Llama-2-7b-hf

# Specify the port
vllm serve meta-llama/Llama-2-7b-hf --port 8100

# Check with --help for more options
# To list all groups
vllm serve --help=listgroup

# To view a argument group
vllm serve --help=ModelConfig

# To view a single argument
vllm serve --help=max-num-seqs

# To search by keyword
vllm serve --help=max

chat

Generate chat completions via the running API server.

Examples:

# Directly connect to localhost API without arguments
vllm chat

# Specify API url
vllm chat --url http://{vllm-serve-host}:{vllm-serve-port}/v1

# Quick chat with a single prompt
vllm chat --quick "hi"

complete

Generate text completions based on the given prompt via the running API server.

Examples:

# Directly connect to localhost API without arguments
vllm complete

# Specify API url
vllm complete --url http://{vllm-serve-host}:{vllm-serve-port}/v1

# Quick complete with a single prompt
vllm complete --quick "The future of AI is"

bench

Run benchmark tests for latency online serving throughput and offline inference throughput.

Available Commands:

vllm bench {latency, serve, throughput}

latency

Benchmark the latency of a single batch of requests.

Example:

vllm bench latency \
    --model meta-llama/Llama-3.2-1B-Instruct \
    --input-len 32 \
    --output-len 1 \
    --enforce-eager \
    --load-format dummy

serve

Benchmark the online serving throughput.

Example:

vllm bench serve \
    --model meta-llama/Llama-3.2-1B-Instruct \
    --host server-host \
    --port server-port \
    --random-input-len 32 \
    --random-output-len 4  \
    --num-prompts  5

throughput

Benchmark offline inference throughput.

Example:

vllm bench throughput \
    --model meta-llama/Llama-3.2-1B-Instruct \
    --input-len 32 \
    --output-len 1 \
    --enforce-eager \
    --load-format dummy

collect-env

Start collecting environment information.

vllm collect-env

run-batch

Run batch prompts and write results to file.

Examples:

# Running with a local file
vllm run-batch \
    -i offline_inference/openai_batch/openai_example_batch.jsonl \
    -o results.jsonl \
    --model meta-llama/Meta-Llama-3-8B-Instruct

# Using remote file
vllm run-batch \
    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/offline_inference/openai_batch/openai_example_batch.jsonl \
    -o results.jsonl \
    --model meta-llama/Meta-Llama-3-8B-Instruct

More Help

For detailed options of any subcommand, use:

vllm <subcommand> --help