An expert assistant for deploying and optimizing large language models using the high-throughput vLLM inference engine.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install vllm
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install vllm using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is vLLM High-Performance Inference Engine?
The vLLM skill for Openclaw Skills empowers developers to deploy large language models with industry-leading efficiency. By leveraging PagedAttention—a breakthrough KV cache management technique inspired by virtual memory in operating systems—vLLM increases GPU memory utilization by 2-4 times compared to traditional methods.
This skill provides the technical expertise needed to manage complex inference workflows, offering up to 24x higher throughput than HuggingFace Transformers. It is designed for production environments where low latency and high concurrency are non-negotiable, supporting advanced features like Continuous Batching and Speculative Decoding.
vLLM High-Performance Inference Engine Use Cases
- Scaling production-grade LLM APIs with high concurrent request volumes.
- Optimizing GPU memory usage for large-scale models like Llama 3.1 405B or DeepSeek V3.
- Reducing latency in multi-turn conversations through Prefix Caching.
- Deploying OpenAI-compatible API servers for seamless integration with existing AI ecosystems.
- Implementing model quantization (AWQ/GPTQ/FP8) to run large models on limited hardware.
How vLLM High-Performance Inference Engine Works
- Environment Initialization: Verifies CUDA compatibility and installs the vLLM engine and dependencies.
- Model Selection: Identifies the optimal model architecture and parameters (e.g., Llama, Qwen, Mistral) based on hardware availability.
- Memory Management: Configures PagedAttention and GPU memory utilization ratios to prevent Out-of-Memory (OOM) errors.
- Optimization Layer: Applies throughput-enhancing techniques such as Tensor Parallelism for multi-GPU setups and Prefix Caching for shared prompts.
- API Provisioning: Launches an OpenAI-compatible server that handles dynamic request batching and high-speed generation.
vLLM High-Performance Inference Engine Setup
To start using this vLLM integration within Openclaw Skills, follow these steps:
Install vLLM via pip (requires CUDA 12.1+):
pip install vllm
Recommended Production Deployment via Docker:
docker run --runtime nvidia --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
-p 8000:8000 vllm/vllm-openai:latest \
--model meta-llama/Llama-3.1-8B-Instruct
vLLM High-Performance Inference Engine Data Schema & Taxonomy
The skill manages model artifacts and runtime configurations as follows:
| Data Type | Storage/Format | Description |
|---|---|---|
| Model Weights | Safetensors / Bin | Cached in ~/.cache/huggingface by default. |
| Inference API | REST / OpenAI | Standardized JSON request/response schema. |
| Quantization | AWQ/GPTQ/FP8 | Supported formats for reducing VRAM footprint. |
| Metadata | Config JSON | Defines tensor parallel size and max model length. |
vLLM High-Performance Inference Engine Advanced Features
- Speculative Decoding: Uses smaller draft models to accelerate the generation speed of larger target models.
- Tensor Parallelism: Native support for distributing models across multiple GPUs using the
--tensor-parallel-sizeflag. - Continuous Batching: Dynamically merges incoming requests to maximize hardware throughput.
- Automatic Prefix Caching: Specifically optimizes multi-turn dialogues and system prompts by reusing KV caches.
- Quantization Support: Seamless integration with AWQ, GPTQ, and FP8 for hardware-specific acceleration on H100 or L40S GPUs.
SKILL.md
Loading
Related Openclaw Skills
Information Extraction
A professional-grade pipeline for extracting entities, relations, and events from raw text into normalized, graph-ready structured formats.
Telegram Send File
A specialized skill for delivering local files, remote URLs, and Telegram file IDs to specific chats and topics via the Bot API.
Code Dev Pipeline
A professional 8-agent collaborative coding pipeline designed for complex software development, frontend design, and automated quality assurance.
HTML to Markdown
A versatile utility to transform HTML code, local files, or live web pages into clean Markdown formatting for AI agents and developers.
Market Intelligence Claw
A real-time strategic intelligence layer that transforms live market data into actionable business insights for ecommerce and digital brands.
DeFi Scout
DeFi Scout delivers real-time on-chain financial intelligence, covering yields, prices, and protocol health across the Optimism and Base ecosystems.
Featured*
