Introduction

Common Compute is a distributed network for batch AI workloads. You ship tasks — embeddings, transcription, video transcode, image generation today, with more rolling out — and get deterministic quotes before anything runs. Tasks execute in sandboxed native runners on idle Apple Silicon Macs.

This guide walks you from install to your first production job in under ten minutes. If you already have an OpenAI, Cohere, or AWS Transcribe integration, you can port in a single line change.

What this is good for

• High-volume batch inference where seconds of latency is acceptable
• Workloads that dominate your AI bill today
• Pipelines with deterministic compute needs and tolerances for retries

What this is not good for

• Sub-100ms realtime inference (use your existing provider)
• Training large models (we dispatch inference only)
• Stateful, long-running processes (tasks are bounded)