# Vllm Bench > Use vllm-bench to benchmark OpenAI-compatible or vLLM serving endpoints, especially multi-turn chat load tests. Use when the user asks for vllm bench, vllm-bench, multi-turn benchmark, chat serving benchmark, TTFT/TPOT/throughput measurement, concurrency sweep, load test, 压测, 多轮压测, or wants help installing, running --help, choosing flags, validating a local PegaInfer/vLLM server, saving JSON results, or interpreting vllm-bench metrics. - Skill: `openinfer-project/vllm-bench-2` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add openinfer-project/vllm-bench-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/openinfer-project/vllm-bench-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: openinfer-project (https://skillmd.com/u/openinfer-project) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/openinfer-project/vllm-bench-2 --- # vllm-bench `vllm-bench` is a Rust benchmark client for online LLM serving. It does not run the model. It drives an already running OpenAI-compatible or vLLM endpoint and reports TTFT, TPOT, ITL, E2EL, throughput, and multi-turn per-turn breakdowns. Use this workflow for multi-turn work: 1. Install or locate `vllm-bench`. 2. Run `vllm-bench --help` and use it as the authority for current flags. 3. Confirm the server base URL and model id from `/v1/models`. 4. Dry-run the dataset locally. 5. Run a tiny smoke benchmark. 6. Scale conversations, turns, and concurrency only after the smoke passes. 7. Save JSON for any result that may be compared later. ## Install Install from the upstream repository: ```bash cargo install --git https://github.com/vllm-project/vllm-bench vllm-bench ``` The trailing `vllm-bench` package name matters: the repo also contains another binary, so omitting it can fail with "multiple packages with binaries found". Cargo installs the binary to `~/.cargo/bin/vllm-bench`. Alternatives from the README: ```bash curl -fsSL https://github.com/vllm-project/vllm-bench/releases/latest/download/vllm-bench-$(uname -m)-linux-musl \ -o vllm-bench && chmod +x vllm-bench git clone https://github.com/vllm-project/vllm-bench.git cd vllm-bench ./install.sh ``` After install, run: ```bash vllm-bench --version vllm-bench --help ``` ## Choose Backend For multi-turn chat, always use: ```bash --backend openai-chat ``` Common backends: | Backend | Endpoint | Use | | --- | --- | --- | | `vllm` / `openai` | `/v1/completions` | text completions | | `openai-chat` | `/v1/chat/completions` | chat, multi-turn, multimodal | | `openai-embeddings` | `/v1/embeddings` | text embeddings | | `vllm-rerank` | `/v1/rerank` | rerank | Prefer `http://127.0.0.1:` over `localhost`; some servers bind IPv4 while `localhost` resolves to IPv6. Confirm the model id before benchmarking: ```bash curl -s http://127.0.0.1:8000/v1/models ``` Use the returned id as `--model`, or omit `--model` and let `vllm-bench` auto-fetch it. ## Multi-Turn Flags Core flags: ```bash --multi-turn --multi-turn-num-turns --num-prompts --multi-turn-concurrency ``` In multi-turn mode, `--num-prompts` means conversation count, not request count. A run with `--num-prompts 50 --multi-turn-num-turns 5` can send up to 250 chat requests. Synthetic random conversations: ```bash --dataset-name random --random-input-len --per-turn-input-len --random-output-len ``` If `--per-turn-input-len` is omitted or `0`, later turns reuse `--random-input-len`. Use `--multi-turn-min-turns` and `--multi-turn-max-turns` when conversation lengths should vary. Think time and prefix sharing: ```bash --multi-turn-delay-ms 500 --multi-turn-prefix-global-ratio 0.2 --multi-turn-prefix-conversation-ratio 0.5 ``` Prefix sharing works only with `--dataset-name random`; the two ratios must sum to less than `1.0`. When either prefix-sharing flag is non-zero, `vllm-bench` switches to fixed- length per-turn prompts with no accumulated chat history. Use this only for prefix-cache experiments; leave both ratios at `0` for realistic multi-turn conversation growth. ## Dry Run Dry-run before sending traffic. It loads the tokenizer and builds conversations without hitting the server: ```bash vllm-bench \ --backend openai-chat \ --model Qwen/Qwen3-4B \ --tokenizer \ --dataset-name random \ --multi-turn --multi-turn-num-turns 2 \ --random-input-len 16 --random-output-len 4 \ --num-prompts 2 \ --dry-run ``` `` is a local tokenizer/model directory; for a public example, replace it with the served model id or any local model directory. ## Example: Generic vLLM Multi-Turn Use this after a vLLM OpenAI server is already running on port 8000: ```bash vllm-bench \ --backend openai-chat \ --base-url http://127.0.0.1:8000 \ --model \ --dataset-name random \ --multi-turn \ --multi-turn-num-turns 5 \ --random-input-len 512 \ --per-turn-input-len 256 \ --random-output-len 128 \ --num-prompts 50 \ --multi-turn-concurrency 10 \ --ready-check-timeout-sec 60 \ --percentile-metrics ttft,tpot,itl,e2el \ --metric-percentiles 50,90,99 \ --save-result ``` Read the output as: - `Conversations completed/total`: end-to-end conversation success. - `Successful requests`: successful turns. - `Total input tokens`: includes growing chat history in normal multi-turn mode. - `per_turn_metrics` in JSON: TTFT/TPOT/ITL/E2EL by turn index. Expect later turns to have larger input token counts because each request sends the prior user/assistant history plus the next user message. ## Example: Local PegaInfer Qwen3-4B Smoke This was validated locally with PegaInfer Qwen3-4B and `vllm-bench` built from this repo. Start PegaInfer: ```bash cd cargo run --release -p pegainfer-server -- \ --model-path \ --served-model-name Qwen/Qwen3-4B \ --port 18080 \ --cuda-graph=false ``` Confirm the model: ```bash curl -s http://127.0.0.1:18080/v1/models ``` Run a tiny multi-turn benchmark: ```bash cd target/release/vllm-bench \ --backend openai-chat \ --base-url http://127.0.0.1:18080 \ --model Qwen/Qwen3-4B \ --tokenizer \ --dataset-name random \ --multi-turn \ --multi-turn-min-turns 2 \ --multi-turn-max-turns 3 \ --random-input-len 32 \ --per-turn-input-len 12 \ --random-output-len 8 \ --num-prompts 3 \ --multi-turn-concurrency 2 \ --ready-check-timeout-sec 30 \ --extra-body '{"min_tokens":null}' \ --percentile-metrics ttft,tpot,itl,e2el \ --metric-percentiles 50,90 \ --save-result \ --result-dir /tmp \ --result-filename vllm-bench-multi-turn-smoke.json ``` The `--extra-body '{"min_tokens":null}'` is for PegaInfer compatibility. The random multi-turn path auto-adds `min_tokens` unless overridden; this pins output length on vLLM but PegaInfer currently rejects that field. `--ignore-eos` also skips `min_tokens` and worked in local smoke testing, but it asks the server to ignore EOS and can grow contexts more aggressively. The local smoke run completed 3/3 conversations, 8/8 turns, and wrote `/tmp/vllm-bench-multi-turn-smoke.json` with `per_turn_metrics`. ## Scale Safely After the smoke passes: 1. Increase `--num-prompts` first to improve measurement stability. 2. Increase `--multi-turn-concurrency` to find the service knee. 3. Increase `--random-input-len`, `--per-turn-input-len`, and `--random-output-len` to match the target workload. 4. Save every serious run with `--save-result --metadata KEY=VALUE`. For deterministic latency A/B runs, pass `--temperature 0` explicitly. Omit it only when the benchmark is intentionally measuring server default generation behavior. Use a sweep when searching for the concurrency knee: ```bash vllm-bench \ --backend openai-chat \ --base-url http://127.0.0.1:8000 \ --model \ --dataset-name random \ --multi-turn --multi-turn-num-turns 5 \ --random-input-len 512 --per-turn-input-len 256 --random-output-len 128 \ --num-prompts 200 \ --sweep-max-concurrency 1,2,4,8,16,32 \ --sweep-summary-percentiles 90,99 \ --save-result ``` For A/B comparisons, keep the workload flags identical and compare result JSON: ```bash vllm-bench --compare baseline.json candidate.json ``` ## Troubleshooting - `min_tokens is not supported by this engine`: pass `--extra-body '{"min_tokens":null}'` or, if appropriate, `--ignore-eos`. - All-zero metrics with failed requests: inspect the first HTTP error printed above the result block; it usually names the unsupported request field. - Connection fails on `localhost`: switch to `127.0.0.1`. - Model id mismatch: use `curl /v1/models` and set `--model` to that exact id. - Tokenizer load fails: set `--tokenizer` to a local model directory with `tokenizer.json`, or rely on server-side tokenize/detokenize fallback if the server implements it. - Dataset construction is suspicious: run the same command with `--dry-run`. - Long serious runs need reproducibility: set `--seed`, `--result-filename`, and `--metadata` entries for server commit, model, GPU, and key flags. ## Metrics Use database-style thinking: requests are transactions, prompt tokens are read set size, output tokens are write volume, and concurrency is the number of in-flight conversations. - TTFT: queueing plus prefill latency until the first output token. - TPOT: decode time per output token after the first token. - ITL: per-token latency distribution. - E2EL: total turn latency. - Request throughput: turns per second. - Output throughput: generated tokens per second. - Multi-turn JSON: inspect `per_turn_metrics` to see how latency changes as history grows.