Run OpenClaw on AMD GPUs with Free 192GB MI300X Access

Set up OpenClaw AI agent with AMD Instinct MI300X GPUs using ROCm and vLLM on AMD Developer Cloud - free tier included.
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6 min read
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Mark
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AI Tools

Problem: OpenClaw Costs Add Up Fast with API Models

You set up OpenClaw and it's working great, but your Claude or GPT API bills are climbing. You want to run powerful open-source models locally, but consumer GPUs max out at 24GB - not enough for modern 139B parameter models.

You'll learn:

  • How to get free AMD MI300X GPU access (192GB memory)
  • Installing vLLM with ROCm optimization for AMD hardware
  • Configuring OpenClaw to use your self-hosted model
  • Running MiniMax-M2.1 (139B parameters) at enterprise scale

Time: 35 min | Level: Intermediate

Why This Works

AMD Developer Cloud provides MI300X instances with 192GB HBM3 memory for free (with $100 starter credits). This is 8x more memory than an RTX 4090, letting you run massive models that would otherwise require API access.

Common use cases:

  • Reducing AI assistant costs from $50+/month to near-zero
  • Running tool-calling models (MiniMax-M2.1) with 194K context windows
  • Self-hosting for privacy and data control
  • Testing enterprise-grade hardware before buying

What you need:

  • OpenClaw installed (any OS - Mac, Windows, Linux)
  • AMD Developer Cloud account (free signup)
  • SSH client
  • Basic Terminal skills

Solution

Step 1: Get AMD Developer Cloud Access

Sign up at the AMD Developer Program to receive $100 in free credits (roughly 50 hours of MI300X usage).

# Visit the signup page
https://www.amd.com/en/developer.html

Expected: Email confirmation with credit activation within 24 hours.

Bonus perks:

  • 1-month DeepLearning.AI Premium membership
  • Monthly hardware sweepstakes entry
  • Free AMD training courses

Step 2: Create MI300X GPU Instance

Log into the AMD Developer Cloud dashboard and create a new droplet.

Configuration:

  • Hardware: MI300X (single instance)
  • Image: ROCm Software (latest version)
  • SSH Key: Add your public key (instructions on setup page)
# Generate SSH key if you don't have one
ssh-keygen -t ed25519 -C "your_email@example.com"

# Copy your public key
cat ~/.ssh/id_ed25519.pub

Expected: Droplet provisioning takes 2-3 minutes. You'll receive an IP address.

If it fails:

  • No credit: Verify email confirmation completed
  • Key rejected: Ensure you copied the .pub file, not private key

Step 3: Connect and Install vLLM

SSH into your droplet and set up the Python environment.

# Connect to your instance
ssh root@<your-droplet-ip>

# Create isolated environment
apt install python3.12-venv
python3 -m venv .venv
source .venv/bin/activate

Install the ROCm-optimized vLLM build with CK Flash Attention support.

# Install vLLM with ROCm support
pip install vllm==0.15.0+rocm700 \
  --extra-index-url https://wheels.vllm.ai/rocm/0.15.0/rocm700

Why this specific version: ROCm 7.0 includes optimized flash attention for MI300X hardware, giving 2-3x faster inference than generic builds.

Expected: Installation takes 5-7 minutes. Final size is about 4GB.

Step 4: Launch Model Server

Start vLLM serving the MiniMax-M2.1 model (139B parameters in FP8).

# Start the server (runs in foreground)
vllm serve MiniMax-01/MiniMax-M2.1-FP8-Dynamic \
  --host 0.0.0.0 \
  --port 8090 \
  --served-model-name MiniMax-M2.1 \
  --max-model-len 194000 \
  --enable-auto-tool-choice \
  --dtype auto

Key flags explained:

  • --enable-auto-tool-choice: Enables native function calling for OpenClaw
  • --max-model-len 194000: Uses full 194K context window
  • --dtype auto: Automatically selects FP8 for 192GB memory efficiency

Expected: Model downloads (takes 15-20 minutes first time), then you'll see:

INFO: Started server process
INFO: Application startup complete
INFO: Uvicorn running on http://0.0.0.0:8090

If it fails:

  • Out of memory: Model is too large; use --max-model-len 128000 instead
  • Port in use: Change --port 8090 to another number (e.g., 8091)

Step 5: Configure OpenClaw

Open a new terminal on your local machine and run the OpenClaw onboarding if you haven't already.

# First-time setup
openclaw onboard --install-daemon

# Or skip to dashboard
openclaw dashboard

Navigate to Settings > Config in the web UI.

Add the Model:

  • API: openai-completions
  • Base URL: http://<your-droplet-ip>:8090/v1
  • Context Window: 194000
  • Model ID: MiniMax-M2.1

Click Apply.

Step 6: Set as Primary Model

Go to the Agents section in OpenClaw settings.

Change Primary Model to:

vllm/MiniMax-M2.1

Why this format: The vllm/ prefix tells OpenClaw to use the vLLM endpoint you configured, not an API service.

Click Apply and wait for the agent to reload (takes 10-15 seconds).

Step 7: Test the Connection

Send a test message to verify everything works.

# Via CLI
openclaw message send --target <your-channel> \
  --message "What model are you using?"

# Or use the web dashboard chat

Expected response:

I'm running on MiniMax-M2.1, a 139B parameter model hosted on 
AMD MI300X hardware via vLLM. How can I help you today?

If it fails:

  • Timeout: Check firewall rules on AMD Cloud (port 8090 must be open)
  • Model not found: Verify the model ID matches exactly in both vLLM and OpenClaw config
  • Tool calling errors: Ensure --enable-auto-tool-choice flag was used when starting vLLM

Verification

Test the tool-calling capabilities that OpenClaw relies on.

# Ask it to perform a task
"Create a text file called test.txt with today's date"

You should see: OpenClaw executes the bash command and confirms file creation. This proves tool calling is working correctly.

Check vLLM logs on your droplet:

# In SSH session
tail -f /path/to/vllm.log  # if you redirected output
# Or just observe terminal output

Expected: You'll see JSON function calls being processed, not just text completions.

Cost Management

Free tier usage:

  • $100 credit = ~50 hours of MI300X time
  • Average chat session: 2-3 hours
  • Heavy usage: 20-25 full sessions before credit depletion

Extending free access:

  • Share projects on social media (tag @AMD) for bonus credits
  • Publish tutorials or demos on GitHub
  • Participate in AMD community forums

Paid tier: After credits expire, MI300X costs approximately $2.50-3.00/hour (significantly cheaper than equivalent API costs for heavy users).

Auto-shutdown tip:

# Set a cron job to stop vLLM after inactivity
# (prevents burning credits while idle)

What You Learned

  • AMD MI300X provides 192GB memory vs 24GB consumer GPUs
  • vLLM with ROCm optimization runs 2-3x faster than generic builds
  • MiniMax-M2.1 supports native tool calling for OpenClaw
  • Free tier provides ~50 hours of enterprise GPU access

Limitations:

  • Requires active internet connection (cloud-based)
  • Initial model download takes 15-20 minutes
  • Free credits expire (need to apply for more or switch to paid)

When NOT to use this:

  • If you're only sending 10-20 messages/day (API is cheaper)
  • If you need offline/air-gapped operation
  • If your use case doesn't require 139B parameter models

Alternative: Consumer AMD GPUs

Can you run this on local AMD GPUs?

Yes, but with major limitations:

Supported consumer cards:

  • RX 7900 XTX (24GB) - Can run 7B-13B models only
  • RX 6800 XT (16GB) - Up to 7B models
  • RX 7600 (8GB) - Not recommended for LLMs

ROCm support status (Feb 2026):

  • Windows: Public preview (PyTorch only)
  • Linux: Full support for RDNA 2/3 architectures
  • Requires ROCm 6.0+ installation

Local setup would use:

# Install ROCm on Ubuntu/Arch Linux
# Then same vLLM installation
pip install vllm --extra-index-url https://wheels.vllm.ai/rocm/...

# But max model size limited by your GPU memory
vllm serve meta-llama/Llama-3.1-8B-Instruct \
  --max-model-len 32000  # Not 194K like MI300X

Reality check: For OpenClaw's advanced features, you need at least 40GB VRAM. Consumer AMD cards don't meet this requirement. The MI300X cloud approach is currently the only viable AMD solution for running enterprise-scale models with OpenClaw.

Tested on AMD MI300X via Developer Cloud, OpenClaw v1.9.2, vLLM 0.15.0+rocm700, Ubuntu 22.04 LTS