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ROCm vLLM Deployment

ROCm vLLM Deployment for Openclaw

A production-ready automation tool for deploying vLLM inference services on AMD ROCm GPUs using Docker Compose and automated environment verification.

alexhegit

v1.0.0

Mar 1, 2026

Install

Information

Skill.md

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.


                  npx clawhub@latest install rocm-vllm-deployment

2. Manual Installation

Copy the skill folder to one of these locations

Global

~/.openclaw/skills/

Workspace

<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.


                  Help me install rocm-vllm-deployment 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 ROCm vLLM Deployment?

The ROCm vLLM Deployment skill is a sophisticated automation framework designed to streamline the hosting of Large Language Models on AMD hardware. By leveraging Openclaw Skills, developers can automate the entire lifecycle of an inference service, from initial environment validation to final functional testing. This skill eliminates the manual complexity of configuring ROCm drivers, managing Docker Compose manifests, and calculating GPU memory requirements.

Built with a focus on reliability and security, this tool ensures that production environments are consistent and optimized. It handles the intricate details of model parameter detection and VRAM estimation, allowing teams to focus on building applications rather than managing infrastructure. Integrating this into your workflow via Openclaw Skills provides a standardized way to manage high-performance AI workloads on Linux-based AMD systems.

ROCm vLLM Deployment Use Cases

Deploying high-performance LLM inference servers on AMD Instinct or Radeon GPUs using Docker.
Automating the setup of vLLM in CI/CD pipelines for scalable AI application development.
Scaling private, self-hosted AI infrastructure with secure credential management.
Rapidly benchmarking and testing various HuggingFace models with automated parameter detection.

How ROCm vLLM Deployment Works

Scans the host system to verify ROCm driver installation, GPU accessibility, and necessary software dependencies.
Securely downloads the target model from HuggingFace using environment-based authentication tokens.
Parses model configuration files to automatically determine optimal execution parameters like tensor parallel size and sequence length.
Generates a tailored docker-compose.yml and secure environment files in a dedicated model directory.
Launches the vLLM container and monitors initialization to ensure the API server reaches a ready state.
Performs automated health checks and functional API tests to verify the integrity of the inference service.
Compiles a detailed deployment report including performance metrics, load times, and resource utilization.

ROCm vLLM Deployment Setup

To deploy your first model using Openclaw Skills, ensure your environment is configured with the necessary HuggingFace credentials.

# Configure HuggingFace access and model storage
export HF_TOKEN="hf_your_access_token"
export HF_HOME="/path/to/your/model/cache"
source ~/.bash_profile

# The skill will automatically detect these variables during execution

Ensure your system has Docker and the AMD ROCm kernel drivers installed prior to initiating the deployment task.

ROCm vLLM Deployment Data Schema & Taxonomy

The skill maintains a structured output directory for every model deployed under the $HOME/vllm-compose/ root. This ensures that Openclaw Skills can easily manage and audit multiple deployments.

File	Purpose
`deployment.log`	Stores all stdout and stderr output from the deployment process.
`test-results.json`	Contains the JSON response from the post-deployment functional test.
`docker-compose.yml`	The specific Docker configuration used to run the vLLM instance.
`DEPLOYMENT_REPORT.md`	A human-readable summary of metrics, status, and troubleshooting commands.
`.env`	A restricted-access file containing sensitive runtime environment variables.

ROCm vLLM Deployment Advanced Features

Intelligent VRAM estimation that predicts memory requirements to prevent OOM errors before they happen.
Automated environment repair that can detect and prompt for missing system packages or group permissions.
Secure token masking in logs to prevent sensitive HuggingFace credentials from being exposed in plain text.
Multi-GPU support with automatic tensor parallel size validation based on model architecture.
Detailed telemetry gathering that captures model load times and active GPU memory consumption.