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Bench Serving Guide

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This guide explains how to benchmark online serving throughput and latency using python -m sglang.bench_serving. It supports multiple inference backends via OpenAI-compatible and native endpoints, and produces both console metrics and optional JSONL outputs.

What it does

  • Generates synthetic or dataset-driven prompts and submits them to a target serving endpoint
  • Measures throughput, time-to-first-token (TTFT), inter-token latency (ITL), per-request end-to-end latency, and more
  • Supports streaming or non-streaming modes, rate control, and concurrency limits

Supported backends and endpoints

  • sglang / sglang-native: POST /generate
  • sglang-oai, vllm, lmdeploy: POST /v1/completions
  • sglang-oai-chat, vllm-chat, lmdeploy-chat: POST /v1/chat/completions
  • trt (TensorRT-LLM): POST /v2/models/ensemble/generate_stream
  • gserver: Custom server (Not Implemented yet in this script)
  • truss: POST /v1/models/model:predict

If --base-url is provided, requests are sent to it. Otherwise, --host and --port are used. When --model is not provided, the script will attempt to query GET /v1/models for an available model ID (OpenAI-compatible endpoints).

Prerequisites

  • Python 3.10+
  • Dependencies typically used by this script: aiohttp, numpy, requests, tqdm, transformers, and for some datasets datasets, pillow, pybase64. Install as needed.
  • An inference server running and reachable via the endpoints above
  • If your server requires authentication, set environment variable OPENAI_API_KEY (used as Authorization: Bearer <key>)

Quick start

Run a basic benchmark against an sglang server exposing /generate:

bash
python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct
bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --num-prompts 1000 \
  --model meta-llama/Llama-3.1-8B-Instruct

Or, using an OpenAI-compatible endpoint (completions):

bash
python3 -m sglang.bench_serving \
  --backend vllm \
  --base-url http://127.0.0.1:8000 \
  --num-prompts 1000 \
  --model meta-llama/Llama-3.1-8B-Instruct

Datasets

Select with --dataset-name:

  • sharegpt (default): loads ShareGPT-style pairs; optionally restrict with --sharegpt-context-len and override outputs with --sharegpt-output-len
  • random: random text lengths; sampled from ShareGPT token space
  • random-ids: random token ids (can lead to gibberish)
  • image: generates images and wraps them in chat messages; supports custom resolutions, multiple formats, and different content types
  • generated-shared-prefix: synthetic dataset with shared long system prompts and short questions
  • mmmu: samples from MMMU (Math split) and includes images

Common dataset flags:

  • --num-prompts N: number of requests

  • --random-input-len, --random-output-len, --random-range-ratio: for random/random-ids/image

  • --image-count: Number of images per request (for image dataset).

  • --apply-chat-template: apply tokenizer chat template when constructing prompts

  • --dataset-path PATH: file path for ShareGPT json; if blank and missing, it will be downloaded and cached

Generated Shared Prefix flags (for generated-shared-prefix):

  • --gsp-num-groups
  • --gsp-prompts-per-group
  • --gsp-system-prompt-len
  • --gsp-question-len
  • --gsp-output-len

Image dataset flags (for image):

  • --image-count: Number of images per request
  • --image-resolution: Image resolution; supports presets (4k, 1080p, 720p, 360p) or custom 'heightxwidth' format (e.g., 1080x1920, 512x768)
  • --image-format: Image format (jpeg or png)
  • --image-content: Image content type (random or blank)

Examples

  1. To benchmark image dataset with 3 images per request, 500 prompts, 512 input length, and 512 output length, you can run:
bash
python -m sglang.launch_server --model-path Qwen/Qwen2.5-VL-3B-Instruct --disable-radix-cache
bash
python -m sglang.bench_serving \
    --backend sglang-oai-chat \
    --dataset-name image \
    --num-prompts 500 \
    --image-count 3 \
    --image-resolution 720p \
    --random-input-len 512 \
    --random-output-len 512
  1. To benchmark random dataset with 3000 prompts, 1024 input length, and 1024 output length, you can run:
bash
python -m sglang.launch_server --model-path Qwen/Qwen2.5-3B-Instruct
bash
python3 -m sglang.bench_serving \
    --backend sglang \
    --dataset-name random \
    --num-prompts 3000 \
    --random-input 1024 \
    --random-output 1024 \
    --random-range-ratio 0.5

Choosing model and tokenizer

  • --model is required unless the backend exposes GET /v1/models, in which case the first model ID is auto-selected.
  • --tokenizer defaults to --model. Both can be HF model IDs or local paths.
  • For ModelScope workflows, setting SGLANG_USE_MODELSCOPE=true enables fetching via ModelScope (weights are skipped for speed).
  • If your tokenizer lacks a chat template, the script warns because token counting can be less robust for gibberish outputs.

Rate, concurrency, and streaming

  • --request-rate: requests per second. inf sends all immediately (burst). Non-infinite rate uses a Poisson process for arrival times.
  • --max-concurrency: caps concurrent in-flight requests regardless of arrival rate.
  • --disable-stream: switch to non-streaming mode when supported; TTFT then equals total latency for chat completions.

Other key options

  • --output-file FILE.jsonl: append JSONL results to file; auto-named if unspecified
  • --output-details: include per-request arrays (generated texts, errors, ttfts, itls, input/output lens)
  • --extra-request-body '&#123;"top_p":0.9,"temperature":0.6&#125;': merged into payload (sampling params, etc.)
  • --disable-ignore-eos: pass through EOS behavior (varies by backend)
  • --warmup-requests N: run warmup requests with short output first (default 1)
  • --flush-cache: call /flush_cache (sglang) before main run
  • --profile: call /start_profile and /stop_profile (requires server to enable profiling, e.g., SGLANG_TORCH_PROFILER_DIR)
  • --lora-name name1 name2 ...: randomly pick one per request and pass to backend (e.g., lora_path for sglang)
  • --tokenize-prompt: send integer IDs instead of text (currently supports --backend sglang only)

Authentication

If your target endpoint requires OpenAI-style auth, set:

bash
export OPENAI_API_KEY=sk-...yourkey...

The script will add Authorization: Bearer $OPENAI_API_KEY automatically for OpenAI-compatible routes.

Metrics explained

Printed after each run:

  • Request throughput (req/s)
  • Input token throughput (tok/s) - includes both text and vision tokens
  • Output token throughput (tok/s)
  • Total token throughput (tok/s) - includes both text and vision tokens
  • Total input text tokens and Total input vision tokens - per-modality breakdown
  • Concurrency: aggregate time of all requests divided by wall time
  • End-to-End Latency (ms): mean/median/std/p99 per-request total latency
  • Time to First Token (TTFT, ms): mean/median/std/p99 for streaming mode
  • Inter-Token Latency (ITL, ms): mean/median/std/p95/p99/max between tokens
  • TPOT (ms): Token processing time after first token, i.e., (latency - ttft)/(tokens-1)
  • Accept length (sglang-only, if available): speculative decoding accept length

The script also retokenizes generated text with the configured tokenizer and reports "retokenized" counts.

JSONL output format

When --output-file is set, one JSON object is appended per run. Base fields:

  • Arguments summary: backend, dataset, request_rate, max_concurrency, etc.
  • Duration and totals: completed, total_input_tokens, total_output_tokens, retokenized totals
  • Throughputs and latency statistics as printed in the console
  • accept_length when available (sglang)

With --output-details, an extended object also includes arrays:

  • input_lens, output_lens
  • ttfts, itls (per request: ITL arrays)
  • generated_texts, errors

End-to-end examples

  1. sglang native /generate (streaming):
bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --dataset-name random \
  --random-input-len 1024 --random-output-len 1024 --random-range-ratio 0.5 \
  --num-prompts 2000 \
  --request-rate 100 \
  --max-concurrency 512 \
  --output-file sglang_random.jsonl --output-details
  1. OpenAI-compatible Completions (e.g., vLLM):
bash
python3 -m sglang.bench_serving \
  --backend vllm \
  --base-url http://127.0.0.1:8000 \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --dataset-name sharegpt \
  --num-prompts 1000 \
  --sharegpt-output-len 256
  1. OpenAI-compatible Chat Completions (streaming):
bash
python3 -m sglang.bench_serving \
  --backend vllm-chat \
  --base-url http://127.0.0.1:8000 \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --dataset-name random \
  --num-prompts 500 \
  --apply-chat-template
  1. Images (VLM) with chat template:
bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model your-vlm-model \
  --dataset-name image \
  --image-count 2 \
  --image-resolution 720p \
  --random-input-len 128 --random-output-len 256 \
  --num-prompts 200 \
  --apply-chat-template

4a) Images with custom resolution:

bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model your-vlm-model \
  --dataset-name image \
  --image-count 1 \
  --image-resolution 512x768 \
  --random-input-len 64 --random-output-len 128 \
  --num-prompts 100 \
  --apply-chat-template

4b) 1080p images with PNG format and blank content:

bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model your-vlm-model \
  --dataset-name image \
  --image-count 1 \
  --image-resolution 1080p \
  --image-format png \
  --image-content blank \
  --random-input-len 64 --random-output-len 128 \
  --num-prompts 100 \
  --apply-chat-template
  1. Generated shared prefix (long system prompts + short questions):
bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --dataset-name generated-shared-prefix \
  --gsp-num-groups 64 --gsp-prompts-per-group 16 \
  --gsp-system-prompt-len 2048 --gsp-question-len 128 --gsp-output-len 256 \
  --num-prompts 1024
  1. Tokenized prompts (ids) for strict length control (sglang only):
bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --dataset-name random \
  --tokenize-prompt \
  --random-input-len 2048 --random-output-len 256 --random-range-ratio 0.2
  1. Profiling and cache flush (sglang):
bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --profile \
  --flush-cache
  1. TensorRT-LLM streaming endpoint:
bash
python3 -m sglang.bench_serving \
  --backend trt \
  --base-url http://127.0.0.1:8000 \
  --model your-trt-llm-model \
  --dataset-name random \
  --num-prompts 100 \
  --disable-ignore-eos
  1. Evaluating large-scale KVCache sharing with mooncake trace (sglang only):
bash
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30000 \
  --model model-name \
  --dataset-name mooncake \
  --mooncake-slowdown-factor 1.0 \
  --mooncake-num-rounds 1000 \
  --mooncake-workload conversation|mooncake|agent|synthetic
  --use-trace-timestamps true \
  --random-output-len 256
  1. Fake decode stress testing (PD disaggregation, decode-only):

When benchmarking pure decode performance in a PD disaggregation setup, you can bypass the prefill node entirely by using --fake-prefill. This requires the decode server to be started with --disaggregation-transfer-backend fake:

bash
# Step 1: Start a decode-only server with fake transfer backend
python -m sglang.launch_server \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --disaggregation-mode decode \
  --disaggregation-transfer-backend fake \
  --port 30001

# Step 2: Run bench_serving with --fake-prefill
python3 -m sglang.bench_serving \
  --backend sglang \
  --host 127.0.0.1 --port 30001 \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --dataset-name random \
  --num-prompts 500 \
  --random-input-len 1024 --random-output-len 256 \
  --fake-prefill

Similarly, bench_one_batch_server also supports --fake-prefill:

bash
python3 -m sglang.bench_one_batch_server \
  --base-url http://127.0.0.1:30001 \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --batch-size 32 --input-len 1024 --output-len 256 \
  --fake-prefill

The --fake-prefill flag automatically injects special sentinel values into each request, telling the decode server to skip real KV transfer and generate fake KV data locally.

Troubleshooting

  • All requests failed: verify --backend, server URL/port, --model, and authentication. Check warmup errors printed by the script.
  • Throughput seems too low: adjust --request-rate and --max-concurrency; verify server batch size/scheduling; ensure streaming is enabled if appropriate.
  • Token counts look odd: prefer chat/instruct models with proper chat templates; otherwise tokenization of gibberish may be inconsistent.
  • Image/MMMU datasets: ensure you installed extra deps (pillow, datasets, pybase64).
  • Authentication errors (401/403): set OPENAI_API_KEY or disable auth on your server.

Notes

  • The script raises the file descriptor soft limit (RLIMIT_NOFILE) to help with many concurrent connections.
  • For sglang, /server_info is queried post-run to report speculative decoding accept length when available.