How can I optimize my AgentAI performance on a Hostinger VPS?

Below are several suggestions to help potentially reduce response times:

1. CPU Resources

Your container’s CPU usage (104.76%) indicates that your 2‑vCPU machine might be getting fully leveraged. Consider the following:

• Scaling Up: Upgrading to more vCPUs can provide additional headroom when running inference-heavy models.
• Container CPU Limits: Check if any CPU limits have been set in your Docker Compose file. Removing or adjusting them might help, if system resources allow.

2. Memory and Swap Usage

While your memory appears largely available (only around 1.2 GiB used), the use of swap (1.0 GiB used on a 2.0 GiB swap file) may add delays because swapping is much slower than RAM access.

• Increase Physical Memory or Allocate More Swap: Consider increasing the VPS’s RAM or size of the swap file temporarily. However, ideally, you want to prevent swapping during inference by having enough real memory available or optimizing memory usage in your container.

3. Docker and Container Configuration

Review your Docker configuration to ensure optimal performance:

• Resource Reservations: n8n’s documentation suggests specific resource allocations for different workloads. Even though AgentAI isn’t part of n8n’s core, similar principles apply. For instance, check that your Docker container isn’t starved by setting resource reservations according to your workload requirements.
• Volume Performance: You’re using NVM storage, which usually offers good IOPS, but verifying disk I/O isn’t a bottleneck can also help.

4. Model Inference and GPU Considerations

The models you’re using (llama, deepseek, mistral) can be very resource intensive. If your inferences don’t need to happen on CPU alone:

• GPU Acceleration: If your VPS or another available machine has a CUDA-capable GPU, consider running your model container with GPU support. Follow the Docker guidelines to install the NVIDIA Container Toolkit and update your Docker Compose file with GPU access. This can significantly reduce inference times.

5. Check Application-Level Configurations

Since AgentAI is built on top of n8n (or uses a similar workflow), also review:

• Execution Process and Node Configuration: Ensure that the settings for processing workflow items (and the model node calls) align with best practices. Sub-optimal configuration in any part of the workflow (e.g., excessive data copying in code nodes) can add latency.
• Log Analysis: Check your container and application logs to identify any delays or errors during model invocation.

Summary

In short, the high CPU usage, modest memory availability with swap activity, and the inference-heavy nature of your models suggest that:

• Upgrading CPU capacity (or migrating to a host with more cores) is likely needed to improve throughput.
• Minimizing reliance on swap by ensuring enough physical memory can reduce latency.
• If possible, leveraging a GPU (and configuring Docker to support it) may provide the biggest performance boost for model inference.

By addressing these points one at a time, you should be able to narrow down the main performance bottlenecks in your AgentAI setup.