How to solve uneven load on OpenClaw in multi-GPU servers

You can solve uneven load in OpenClaw on multi-GPU servers by tuning your configuration for balanced resource use. Uneven load slows down your models and leaves some GPUs idle while others work too hard. When you fix this, you get faster results and better hardware use. Take a close look at your current setup and prepare to make changes that boost your server’s performance.

Diagnosing Uneven Load

Signs of Imbalance in OpenClaw

You can spot uneven load in OpenClaw by watching how your GPUs perform. When you see one GPU working much harder than others, you know something is wrong. You may notice slow response times or tasks piling up on a single device. Sometimes, your server logs show that one GPU handles most of the requests while others sit idle. You might also see memory usage spikes on one GPU, which can lead to crashes or errors.

Here are some signs you should look for:

• One GPU runs at high temperature while others stay cool.
• Task completion times vary greatly between GPUs.
• VRAM usage is much higher on one device.
• Server logs show repeated warnings about resource overload.

Common Causes in Multi-GPU Setups

You often face uneven load because of mistakes in configuration or hardware limits. Sometimes, OpenClaw does not split tasks evenly across all GPUs. You may set up your CUDA cores incorrectly, or forget to balance VRAM allocation. Network delays can also cause one GPU to get more work than others.

The table below shows common causes and their effects:

You should check your setup for these issues. Fixing them helps you avoid uneven load and keeps your server running smoothly.

Configuration and Setup for Balanced Load

Set CUDA Core Number

You can control how OpenClaw assigns tasks to each GPU by setting the CUDA core number. This step helps you prevent improper task splitting, which often leads to one GPU doing most of the work. When you assign CUDA cores explicitly, you make sure each GPU receives a fair share of the workload.

To set CUDA core numbers, follow these steps:

1. Identify the number of GPUs in your server using nvidia-smi.
2. Open your OpenClaw configuration file.
3. Assign each model or task to a specific CUDA device.
   For example:

   models:
     - name: model_A
       device: cuda:0
     - name: model_B
       device: cuda:1

4. Save the configuration and restart OpenClaw.

Adjust VRAM Settings

VRAM, or video memory, plays a big role in how well your GPUs handle tasks. If one GPU runs out of VRAM, it can slow down or even crash, while others remain underused. You can avoid this by adjusting VRAM settings to balance model loading across all GPUs.

Here is how you can adjust VRAM settings:

• Check the VRAM available on each GPU with nvidia-smi.
• In your OpenClaw configuration, set memory limits for each model.
  Example:

  models:
    - name: model_A
      device: cuda:0
      memory_limit: 8GB
    - name: model_B
      device: cuda:1
      memory_limit: 8GB

• Make sure the total memory used on each GPU does not exceed its capacity.

Note: Even VRAM allocation helps prevent uneven load and keeps your server stable.

Manual Model Parameter Selection

Manual model parameter selection gives you more control over how each GPU works. You can choose batch size, precision, and other settings for each model. This step ensures that no GPU gets overloaded or underused.

Follow these guidelines for manual parameter selection:

• Set batch sizes that match each GPU’s capability.
• Adjust precision (FP16 or FP32) based on the GPU’s support.
• Assign heavier models to more powerful GPUs.

For example:

models:
  - name: model_A
    device: cuda:0
    batch_size: 32
    precision: FP16
  - name: model_B
    device: cuda:1
    batch_size: 16
    precision: FP32

Callout: Manual tuning takes time, but it pays off. You avoid bottlenecks and make the most of your hardware.

When you set CUDA core numbers, adjust VRAM settings, and select model parameters manually, you create a balanced environment. These steps help you solve uneven load and get the best performance from your server.

Optimization and Scaling Strategies

Use OpenClaw Load Balancing Features

OpenClaw gives you built-in tools to balance work across all GPUs. You can enable automatic load balancing in the configuration file. This feature helps you avoid uneven load by letting OpenClaw decide how to split tasks. You do not need to assign every job by hand. OpenClaw checks the status of each GPU and sends new tasks to the one with the most free resources.

To turn on load balancing, add this to your configuration:

load_balancing:
  enabled: true
  strategy: auto

Tip: Try different strategies like “round-robin” or “least-loaded” to see which works best for your server.

Horizontal Scaling for Load Distribution

Sometimes, one server cannot handle all the requests. You can fix this by adding more servers. This method is called horizontal scaling. You connect several servers together, and each one runs OpenClaw with its own GPUs. A load balancer sits in front and sends tasks to the server with the most capacity.

Horizontal scaling helps you handle more users and keeps performance high. You also reduce the risk of uneven load because tasks spread out over many machines.

Monitoring and Profiling Tools

You need to watch your system to keep it running well. Monitoring tools show you how each GPU works. Profiling tools help you find slow spots in your setup. Use tools like NVIDIA-SMI, OpenClaw’s dashboard, or Prometheus with Grafana. These tools let you track GPU usage, memory, and temperature.

• Set up alerts for high GPU usage.
• Check logs for errors or slowdowns.
• Review graphs to spot trends over time.

Note: Regular monitoring helps you catch problems early and keeps your server balanced.

Troubleshooting Persistent Load Issues

Hardware and Network Bottlenecks

You may notice that even after careful setup, uneven load still happens. Hardware and network bottlenecks often cause this problem. If one GPU runs slower than the others, check its health. Dust, overheating, or aging hardware can reduce speed. You should also compare the PCIe lanes and bandwidth for each one. Sometimes, a GPU connected through a slower slot cannot keep up.

Network issues can also create bottlenecks. If your server connects to other machines or cloud services, high latency or packet loss can slow down task distribution. Place your server in a location with strong connectivity. For example, servers in Hong Kong often provide lower latency for users in Asia.

Tip: Use tools like iperf to test network speed between servers. Replace faulty cables or switches if you find weak spots.

Software Configuration Errors

Software mistakes can lead to persistent uneven load. You need to check your OpenClaw settings and server environment. Here are some steps you can follow:

• Choose a server in a location that reduces latency, such as Hong Kong.
• Make sure your server has enough resources. For basic tasks, use at least a 2-core CPU and 2GB RAM. Upgrade if you run complex models.
• Open TCP access to port 18789. This step allows OpenClaw to communicate properly.
• Set up an IP whitelist for SSH on port 22. This action improves security and prevents unwanted access.
• If you use overseas models, configure a stable proxy service. You can also select servers with optimized routing paths to lower latency.

You should review your configuration files for typos or missing fields. Even a small error can cause OpenClaw to assign tasks unevenly. Restart your services after making changes to apply new settings.

You can resolve uneven load in OpenClaw by setting CUDA cores, adjusting VRAM, and tuning model parameters. Regular monitoring and proactive configuration keep your system balanced and efficient. Stay flexible as hardware and software change. For ongoing success, review resources like LayerStack tutorials, product documentation, and the OpenClaw community. These tools help you adapt and maintain top performance.

FAQ

How do you check if OpenClaw uses all GPUs?

You can run nvidia-smi in your terminal. This command shows GPU usage in real time. You will see each GPU’s activity and memory use. OpenClaw’s dashboard also displays the load.

What should you do if one GPU always runs hotter?

Check your configuration for task assignment errors. Clean the GPU’s fans and ensure good airflow. If the problem continues, test for hardware issues.

Can you add more GPUs to an existing OpenClaw server?

Yes, you can add more GPUs. Update your OpenClaw configuration to include the new devices. Restart the service to apply changes. Make sure your power supply supports the extra ones.

Why does OpenClaw sometimes ignore a GPU?

OpenClaw may skip a GPU if you set the wrong device ID or if the GPU has hardware faults. Double-check your configuration file. Use nvidia-smi to confirm all the work.

How often should you monitor GPU load?

You should check GPU load daily during heavy use. Set up alerts for high temperatures or memory usage. Regular monitoring helps you catch problems early.