vserve 0.2.1

CLI for managing vLLM inference on GPU workstations

vserve

A CLI for managing vLLM inference on GPU workstations.

Download models. Calculate limits. Serve with one command. Control fans.

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Install

uv tool install vserve

Or with pip:

pip install vserve

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Quick Start

vserve init                  # scan GPU, vLLM, CUDA, systemd — write config
vserve download              # search HuggingFace, pick variant, download
vserve tune <model>          # calculate context/concurrency limits for your GPU
vserve start <model>         # interactive config → serve via systemd

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What It Does

vserve manages the full lifecycle of serving LLMs with vLLM on a GPU workstation:

• Download — search HuggingFace, see available weight variants (MXFP4, BF16, GGUF) with sizes, download only what you need
• Tune — calculate exactly what context lengths and concurrency your GPU can handle, based on model architecture and available VRAM
• Start/Stop — interactive config wizard, systemd service management, health check with timeout
• Fan control — temperature-based curve daemon with quiet hours, or hold a fixed speed
• Multi-user — file-based locking, terminal notifications when another user holds the GPU
• Doctor — diagnose GPU, CUDA, vLLM, systemd issues with actionable fix suggestions

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Commands

Command	Description
vserve	Dashboard — GPU, models, status
vserve init	Auto-discover vLLM and write config
vserve download [model]	Search and download from HuggingFace with variant picker
vserve models [name]	List models or show detail (fuzzy match)
vserve tune <model>	Calculate context/concurrency limits for your GPU
vserve start [model]	Configure and start serving
vserve stop	Stop the vLLM service
vserve status	Show current serving config
vserve fan [auto|off|30-100]	GPU fan control with temp-based curve
vserve doctor	Check system readiness

All commands support fuzzy matching — vserve start qwen fp8 finds the right model.

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Prerequisites

Requirement	Check	Install
NVIDIA GPU + drivers	nvidia-smi	nvidia.com/drivers
CUDA toolkit	nvcc --version	sudo apt install nvidia-cuda-toolkit
vLLM 0.18+	vllm --version	docs.vllm.ai
systemd	(most Linux servers)	See troubleshooting
sudo access	for systemctl, fan control

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Configuration

Auto-discovered on first run. Override at ~/.config/vserve/config.yaml:

vllm_root: /opt/vllm
cuda_home: /usr/local/cuda
service_name: vllm
service_user: vllm
port: 8888

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Fan Control

vserve fan              # show status, interactive menu
vserve fan auto         # temp-based curve with quiet hours
vserve fan 80           # hold at 80% (persistent daemon)
vserve fan off          # stop daemon, restore NVIDIA auto

The auto curve ramps with temperature and caps fan speed during quiet hours (configurable). Emergency override at 88C ignores quiet hours.

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Development

git clone https://github.com/Gavin-Qiao/vserve.git
cd vserve
uv sync --dev
uv run pytest tests/              # 175 tests
uv run ruff check src/ tests/     # lint
uv run mypy src/vserve/           # type check

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License

MIT