Here’s a practical Windows 11 + NVIDIA GPU + 64 GB RAM checklist for setting up OpenClaw in WSL2, which is the route current Windows guidance and community writeups keep converging on. The key idea is: Windows hosts the GPU driver, WSL2 hosts Linux, and Docker Desktop bridges the container layer.youtubeskywork+2youtube

Checklist

Windows prep

Install WSL2

wsl --install
wsl --update
wsl --set-default-version 2
wsl --install -d Ubuntu-24.04
wsl --status

WSL2 is the required Linux layer for GPU-backed Docker workflows on Windows 11.developer.nvidiayoutube

Open Ubuntu and prep Linux

sudo apt update && sudo apt upgrade -y
sudo apt install -y curl git ca-certificates gnupg lsb-release

Confirm GPU visibility in WSL

nvidia-smi

If this works, WSL can see the NVIDIA GPU through the Windows driver stack.youtubeforecr

Install Docker Desktop

Verify Docker

docker version
docker run --rm hello-world

Enable NVIDIA GPU passthrough for Docker in WSL

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt update
sudo apt install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker

Then restart WSL:

wsl --shutdown

Test GPU containers:

docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi

This is the important pass/fail test for NVIDIA GPU passthrough in Docker on WSL2.forecryoutubedeveloper.nvidia

Install OpenClaw

In WSL Ubuntu, run the official Linux installer command from OpenClaw’s docs or site. Current Windows guides consistently place this step inside WSL, not native Windows.skyworkyoutube

Typical pattern:

curl -fsSL <OPENCLAW_INSTALL_URL> | bash

If the installer asks about Docker, use the Docker Desktop-backed WSL integration you already enabled.cybernativeyoutube

Start OpenClaw

Use the command OpenClaw’s installer or docs provide for startup. If you’re following the current Windows/WSL guidance, keep the service and agent runtime inside WSL rather than crossing back into Windows filesystem paths too early.youtubecybernative

Lock down WSL

Best-practice notes

Your 64 GB RAM is more than enough for the platform and local models; the real issue is GPU routing and container reachability, not memory. Community reports on OpenClaw/NemoClaw-style setups on Windows show that WSL2 + Docker works best when the model and the agent runtime stay in the same WSL environment.githubyoutubecybernative+1

If you want, I can turn this into a single copy-paste runbook with placeholders like <OPENCLAW_INSTALL_URL> filled in once you share the exact OpenClaw project link.

turn this into a single copy-paste runbook with placeholders like <OPENCLAW_INSTALL_URL> filled in once you share the exact OpenClaw project link.

Absolutely — here is a single copy-paste Windows 11 WSL2 runbook using the official OpenClaw install script URL and the current docs’ Linux/WSL2 flow. I’m assuming you want OpenClaw on Windows 11 + WSL2 Ubuntu + Docker Desktop + NVIDIA GPU.openclaw+2youtube

Copy-paste runbook

1) Windows PowerShell (Admin)

wsl --install
wsl --update
wsl --set-default-version 2
wsl --install -d Ubuntu-24.04
wsl --status

2) Open Ubuntu in WSL, then run

sudo apt update && sudo apt upgrade -y
sudo apt install -y curl git ca-certificates gnupg lsb-release

3) Verify GPU is visible in WSL

nvidia-smi

4) Install Docker Desktop in Windows

5) Install NVIDIA container tooling in WSL

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt update
sudo apt install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker

6) Restart WSL

wsl --shutdown

7) Test Docker GPU passthrough

docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi

8) Install OpenClaw

curl -fsSL https://openclaw.ai/install.sh | bash

If you want to skip onboarding for a moment:

curl -fsSL https://openclaw.ai/install.sh | bash -s -- --no-onboard

The official docs also list npm and GitHub install options if you prefer those paths.openclaw+1

9) Start OpenClaw onboarding

openclaw onboard --install-daemon

10) Verify OpenClaw

openclaw --version openclaw status

NVIDIA GPU notes

The important part is that GPU support comes from the Windows NVIDIA driver into WSL2, then into Docker through the NVIDIA container runtime. If nvidia-smi works in WSL and the CUDA container test works, your GPU passthrough is good.youtubeforecr

If something fails

npm install -g openclaw@latest
openclaw onboard --install-daemon

OpenClaw’s docs and GitHub also show a source install path if needed.github+1

If you want, I can turn this into a one-screen checklist with checkboxes only, or a version that also includes Ollama in the same runbook.

How do I configure OpenClaw agents after installation

After installation, you usually configure OpenClaw agents in three layers: global defaults, per-agent definitions, and verification. The current docs describe openclaw onboard, openclaw config set, openclaw agents add, openclaw agents list, and openclaw config get as the main workflow.meta-intelligence+2

Checklist

Set globals

These commands set the default behavior that new agents inherit:

openclaw config set agents.defaults.model.primary claude-sonnet-4 --global
openclaw config set agents.defaults.model.fallback gpt-4.1-mini --global
openclaw config set agents.defaults.model.temperature 0.5 --global
openclaw config set agents.defaults.model.maxTokens 64000 --global

The docs say OpenClaw stores config in ~/.openclaw/openclaw.json, and global defaults live under agents.defaults.getopenclaw+1

Add agents

Use openclaw agents add to register a new agent. The documented flags include --model, --identity, --workspace, --tools, --template, --description, --max-tokens, --temperature, and --global.meta-intelligence+1

Example:

openclaw agents add architect \
  --model claude-opus-4-6 \
  --workspace ~/projects/my-app \
  --description "System architect — design decisions and technology selection"

You can repeat that pattern for developer, reviewer, or other specialized roles.ai2sql+1

Identity file

Each agent can have an identity file, often agent.md, that defines its persona, rules, and behavior. The docs also mention SOUL.md as a customization option in some setups, but the basic idea is the same: keep the agent’s instructions close to the agent folder.getopenclaw+2

A typical structure looks like this:

~/.openclaw/agents/
  architect/
    agent.md
  reviewer/
    agent.md

Verify config

Use these checks after creating agents:

openclaw agents list --verbose
openclaw config get agents.registered.architect.model
openclaw agents test architect --dry-run

The docs say agents list shows the registered agents, config get confirms the stored model settings, and dry-run testing helps catch bad config before launch.meta-intelligence+1

If you want a simple starting profile

For a first setup, use one default generalist agent plus one specialist:

That matches the multi-agent patterns described in the current OpenClaw guides.lumadock+2