Vlmbench

Skill vlm-run/vlmbench/.claude/skills/vlmbench

VLM benchmark CLI — run, compare, and reproduce VLM inference benchmarks. Use when the user wants to: (1) benchmark a VLM model's throughput, latency, or VRAM usage, (2) compare results across models or configs, (3) choose or configure a backend (vLLM Docker, vLLM native, Ollama, SGLang, cloud API), (4) run text-only LLM benchmarks via HF dataset text columns, (5) look up model-specific --serve-args, or (6) debug server startup or GPU detection issues. Triggers on: vlmbench, VLM benchmarking, OCR model evaluation, inference throughput, tokens/sec, TTFT, TPOT, VRAM, text benchmark.From its SKILL.md

Install

npx -y skills add vlm-run/vlmbench --skill vlmbench

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One thing to look at

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SKILL.md

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vlmbench

All CLI logic is in vlmbench/cli.py (single file). Entry point: main().

Running benchmarks

Prefer uvx — no install needed:

# macOS (Ollama auto-starts)
uvx vlmbench run -m qwen3-vl:2b -i ./images/

# Linux (vLLM Docker auto-starts with --gpus all)
uvx vlmbench run -m Qwen/Qwen3-VL-8B-Instruct -i ./images/

# Linux (native vLLM)
uvx vlmbench run -m Qwen/Qwen3-VL-8B-Instruct -i ./images/ --backend vllm

# Remote/cloud server — model auto-detected from GET /v1/models
uvx vlmbench run -i ./images/ --base-url https://my-server.example.com/v1 --api-key $KEY

# HF image dataset
uvx vlmbench run -m Qwen/Qwen3-VL-2B-Instruct \
  -d hf://vlm-run/FineVision-vlmbench-mini --max-samples 64

# HF text dataset (text-only / LLM benchmark)
uvx vlmbench run -m meta-llama/Llama-3.1-8B-Instruct \
  -d hf://my-org/my-prompts --dataset-text-col prompt --prompt ""

# Compare results
uvx vlmbench compare results/*.json

run is the default subcommand — flags like --model implicitly invoke it.

--model is optional

When --base-url points to a running server, -m / --model can be omitted. vlmbench calls GET /v1/models on the server and uses the first returned model ID. The Authorization: Bearer <api-key> header is included automatically when --api-key is set. --model (or --profile) is still required when using --serve to start a new server.

Text-only / LLM benchmarks

Use --dataset-text-col <col> to load a string column from an HF dataset instead of images. Each row's value is sent as a {"type": "text"} content block before the --prompt instruction.

# Column value IS the full message (no instruction)
--dataset-text-col prompt --prompt ""

# Column value is the document; --prompt is the instruction
--dataset-text-col text --prompt "Summarize in one sentence."

Auto-detection: when no image column is found, vlmbench falls back to any column named text, prompt, input, content, query, question, or instruction.

Backend resolution

Platform	`auto` resolves to	Model ID style
macOS	`ollama`	`qwen3-vl:2b`
Linux	`vllm-openai:latest` (Docker)	`Qwen/Qwen3-VL-2B-Instruct`

Docker backends use --gpus all --ipc=host. Servers run in tmux sessions (vlmbench-vllm, vlmbench-ollama, vlmbench-sglang).

Model-specific serve-args

See MODELS.md for the full table of tested models and their required --serve-args. Reference this file when the user asks which models are supported or how to serve a specific model.

Concurrency sweep

Use --concurrency 4,8,16,32,64 to run at multiple concurrency levels in a single invocation. Produces one JSON per level (tagged c4, c8, etc.) and a consolidated comparison table. A single value (e.g. --concurrency 8) runs at that level only.

Key metrics

TTFT (ms), TPOT (ms), tok/s, img/s, latency s/img, VRAM peak (MiB), prompt/completion token counts. Results saved as JSON to ./results/{backend}-v{version}-{model}-{gpu}-{tag}.json.

GPU detection

CUDA_VISIBLE_DEVICES is respected via _nvidia_gpu_index() -> nvidia-smi --id={idx}. When VRAM or GPU name looks wrong, check this env var first.