docs/content/features/distribution.md
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LocalAI supports distributing inference workloads across multiple machines. There are two approaches, each suited to different use cases:
Production-grade horizontal scaling with centralized management. Frontends are stateless LocalAI instances behind a load balancer; workers self-register and receive backends dynamically via NATS. State lives in PostgreSQL.
Best for: production deployments, Kubernetes, managed infrastructure.
[Read more]({{% relref "features/distributed-mode" %}})
Peer-to-peer networking via libp2p. Share a token to form a cluster with automatic discovery — no central server required. Supports federated load balancing and worker-mode weight sharding.
Best for: ad-hoc clusters, community sharing, quick experimentation.
[Read more]({{% relref "features/distributed_inferencing" %}})
| P2P / Federation | Distributed Mode | |
|---|---|---|
| Discovery | Automatic via libp2p token | Self-registration to frontend URL |
| State storage | In-memory / ledger | PostgreSQL |
| Coordination | Gossip protocol | NATS messaging |
| Node management | Automatic | REST API + WebUI |
| Setup complexity | Minimal (share a token) | Requires PostgreSQL + NATS |