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docs/cli/README.md

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+# 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:
+
+```bash
+vllm --help
+```
+
+Available Commands:
+
+```bash
+vllm {chat,complete,serve,launch,bench,collect-env,run-batch}
+```
+
+## serve
+
+Starts the vLLM OpenAI Compatible API server.
+
+Start with a model:
+
+```bash
+vllm serve meta-llama/Llama-2-7b-hf
+```
+
+Specify the port:
+
+```bash
+vllm serve meta-llama/Llama-2-7b-hf --port 8100
+```
+
+Serve over a Unix domain socket:
+
+```bash
+vllm serve meta-llama/Llama-2-7b-hf --uds /tmp/vllm.sock
+```
+
+Check with --help for more options:
+
+```bash
+# To list all flags
+vllm serve --help=all
+
+# To view an argument group
+vllm serve --help=ModelConfig
+
+# To view a single argument
+vllm serve --help=max-num-seqs
+
+# To search by keyword or flag name
+vllm serve --help=max
+```
+
+!!! tip "Human-readable integer arguments"
+    Many integer arguments accept human-readable suffixes for convenience. For example:
+
+    - `1k` = 1,000 (decimal kilo)
+    - `1K` = 1,024 (binary kibibyte)
+    - `1m` = 1,000,000 (decimal mega)
+    - `1M` = 1,048,576 (binary mebibyte)
+    - `1g` / `1G` = 1 billion / 1 gibibyte
+    - `1t` / `1T` = 1 trillion / 1 tebibyte
+    
+    Decimal suffixes (`k`, `m`, `g`, `t`) also accept floating point: `25.6k` = 25,600.
+    Binary suffixes (`K`, `M`, `G`, `T`) require integers: `32K` = 32,768.
+    
+    Supported arguments include: `--max-model-len`, `--max-num-batched-tokens`, `--max-num-scheduled-tokens`, `--kv-cache-memory-bytes`, `--safetensors-prefetch-block-size`.
+
+See [vllm serve](./serve.md) for the full reference of all available arguments.
+
+## launch
+
+Launch individual vLLM components.
+
+```bash
+# Launch the rendering server component
+vllm launch render meta-llama/Llama-3.2-1B-Instruct
+
+# Inspect all available flags for the render component
+vllm launch render --help=all
+```
+
+See [vllm launch render](./launch/render.md) for the current launch
+component reference.
+
+## chat
+
+Generate chat completions via the running API server.
+
+```bash
+# 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"
+
+# Print TTFT and throughput statistics after each response
+vllm chat --stats
+```
+
+See [vllm chat](./chat.md) for the full reference of all available arguments.
+
+## complete
+
+Generate text completions based on the given prompt via the running API server.
+
+```bash
+# 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"
+
+# Print TTFT and throughput statistics after each response
+vllm complete --stats
+```
+
+See [vllm complete](./complete.md) for the full reference of all available arguments.
+
+## bench
+
+Run benchmark tests for latency online serving throughput and offline inference throughput.
+
+To use benchmark commands, please install with extra dependencies using `pip install vllm[bench]`.
+
+Available Commands:
+
+```bash
+vllm bench {latency, serve, throughput}
+```
+
+### latency
+
+Benchmark the latency of a single batch of requests.
+
+```bash
+vllm bench latency \
+    --model meta-llama/Llama-3.2-1B-Instruct \
+    --input-len 32 \
+    --output-len 1 \
+    --enforce-eager \
+    --load-format dummy
+```
+
+See [vllm bench latency](./bench/latency.md) for the full reference of all available arguments.
+
+### serve
+
+Benchmark the online serving throughput.
+
+```bash
+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
+```
+
+See [vllm bench serve](./bench/serve.md) for the full reference of all available arguments.
+
+### throughput
+
+Benchmark offline inference throughput.
+
+```bash
+vllm bench throughput \
+    --model meta-llama/Llama-3.2-1B-Instruct \
+    --input-len 32 \
+    --output-len 1 \
+    --enforce-eager \
+    --load-format dummy
+```
+
+See [vllm bench throughput](./bench/throughput.md) for the full reference of all available arguments.
+
+## collect-env
+
+Start collecting environment information.
+
+```bash
+vllm collect-env
+```
+
+## run-batch
+
+Run batch prompts and write results to file.
+
+Running with a local file:
+
+```bash
+vllm run-batch \
+    -i features/openai_batch/openai_example_batch.jsonl \
+    -o results.jsonl \
+    --model meta-llama/Meta-Llama-3-8B-Instruct
+```
+
+Using remote file:
+
+```bash
+vllm run-batch \
+    -i https://raw.githubusercontent.com/vllm-project/vllm/main/examples/features/openai_batch/openai_example_batch.jsonl \
+    -o results.jsonl \
+    --model meta-llama/Meta-Llama-3-8B-Instruct
+```
+
+See [vllm run-batch](./run-batch.md) for the full reference of all available arguments.
+
+## More Help
+
+For detailed options of any subcommand, use:
+
+```bash
+vllm <subcommand> --help
+```