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):
bashvllm 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:
bashvllm 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):
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
| 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)
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):
bashvllm 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:
bashvllm 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):
bashvllm 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):
bashdocker 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/--portor--base-urlare correct. - Model not found: Pass
--modelexplicitly or ensure/v1/modelsreturns the model. - URL must end with chat/completions: Use
--endpoint /v1/chat/completionswhen--backend openai-chat. - Rate limit / 429: Reduce
--request-rateor--max-concurrency. - Ready check: Use
--ready-check-timeout-sec 60to wait for the endpoint before benchmarking. - SSL: Use
--insecurefor self-signed certificates.
Notes
- For embeddings/rerank benchmarks, use
--backend openai-embeddings,vllm-pooling, orvllm-rerank. --profilerequires--profiler-configon the server for vLLM profiling.- Goodput SLOs are useful for SLA-style analysis; see DistServe paper for details.
