Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vllm-project/vllm-skills --skill vllm-bench-servegit clone --depth 1 https://github.com/vllm-project/vllm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-bench-serve)<a href="https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-bench-serve"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-bench-serve/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vllm-project/vllm-skills/vllm-bench-serve"><img src="https://agentmods.dev/badge/skills/vllm-project/vllm-skills/vllm-bench-serve.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00093 | $0.01958 |
| Opus 5 | $0.00046 | $0.00979 |
| Sonnet 5 | $0.00019 | $0.00392 |
| Haiku 4.5 | $0.00009 | $0.00196 |
Grade A, and why
vllm-bench-serve scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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):
vllm 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:
vllm 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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 181 lines · 93 tokens per session scan A cb397fad2c5e
vllm-bench-serve is a skill published in the GitHub repository vllm-project/vllm-skills (98 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 93 tokens to every session and 1,958 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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