A simple asynchronous job queue for distributed AI inference
Send your LLM inference request via API or SDK. Specify the model and parameters.
Job enters our distributed queue and waits for an available worker with the right GPU.
A connected worker picks up the job, runs the inference, and submits results back.
Retrieve your completed results via webhook or polling. Simple and efficient.
The key benefits of our distributed approach
Save up to 90% compared to dedicated GPU instances. Pay only for actual compute time, not idle resources.
Process thousands of jobs in parallel across our distributed worker network. No infrastructure to manage.
Simple API, comprehensive SDKs, and detailed documentation. Start integrating in minutes.
A deeper look at how MicroDC processes your jobs
When you submit a job through our API or SDK, it's validated and added to our distributed queue. Each job includes the model specification, input prompt, and any configuration parameters.
Our scheduler matches jobs with the most suitable available workers based on model requirements, GPU capabilities, and current load. This ensures optimal resource utilization.
Workers pull jobs from the queue, load the required model (if not already cached), execute the inference, and submit results back to the platform. All communication is encrypted and authenticated.
Once complete, results are stored and you're notified via webhook (if configured) or you can poll the status. Results remain available for retrieval for a configurable period.
Create a free account and start processing AI jobs in minutes.