Optimizing Moodle with Ollama AI

Modern Learning Management Systems (LMS) like Moodle are entering a new era. By integrating local LLM orchestration via Ollama, we can provide private, performant AI features without the cost of external APIs.

Introduction to LLMs in Education

The educational landscape is shifting. Privacy is paramount when dealing with student data, making cloud-based AI solutions often problematic for strict compliance environments. This is where Ollama shines—allowing us to run powerful models like Llama 3 or Mistral directly on our own infrastructure.

Implementation: The PHP Connector

To connect Moodle (PHP) with Ollama, we leverage the REST API. Below is a simplified implementation of a service class that handles the stream-based communication with the local AI daemon.

class OllamaService {
    // Initialize connection to local Ollama instance
    public function generateResponse(string $prompt) {
        $ch = curl_init('http://localhost:11434/api/generate');
        curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode([
            'model' => 'llama3',
            'prompt' => $prompt,
            'stream' => false
        ]));
        return curl_exec($ch);
    }
}

Benchmarks & Performance

In our tests running on an NVIDIA RTX 4090, we observed tokens-per-second (TPS) rates that exceed the typical latency of cloud-based providers, with the added benefit of zero egress costs.

"The goal isn't just to add AI; it's to add intelligence that respects user privacy and system reliability."