One model split across several devices
“Hive mind” can mean many things in AI. In Local AGI, it has a specific technical meaning: several devices cooperate to run one model. Each member loads a slice of the model, processes its part of the inference, and passes intermediate data to the next member.
The combined hive can then serve a Private Network or the optional Public Network as a single Neuron. To the requesting agent, it behaves like one model provider even though several machines are doing the work.
Hive Mind pools model capacity across devices. It is not a claim that the computers become conscious, and it is not the same as several independent agents debating or delegating tasks.
How a Hive Mind inference moves
Split the model
Different portions of the model are assigned to hive members according to the memory and compute available on each device.
Load one slice per device
Each computer holds its assigned layers instead of every computer loading the complete model.
Pass intermediate data
During inference, output from one slice crosses the connection to the device responsible for the next slice.
Return one result
The hive exposes the distributed model as one Neuron and returns a unified response to the requesting agent.
Why pooled memory matters
A model must fit into available memory along with runtime overhead and working data. On a single device, the largest usable model is therefore capped by that machine. A hive changes the boundary by distributing model weights across several machines.
For example, Local AGI can connect a 16 GB MacBook and a 16 GB Windows PC over an ordinary network cable to run a 35B-class model that neither machine fits alone. This illustrates the capacity benefit; actual usable model size and speed still depend on quantization, runtime overhead, hardware, and connection quality.
The network is part of the computer
Splitting a model increases capacity, but it also introduces communication between model slices. Intermediate data may cross the link repeatedly while tokens are generated. A slow or unstable connection can leave otherwise powerful processors waiting.
| Connection | What to expect | Best use |
|---|---|---|
| Wired local network | Lower latency and more consistent throughput. | Preferred when hive members are in the same location. |
| Fast local Wi-Fi | Convenient, but performance can vary with congestion and signal quality. | Testing and lighter distributed workloads. |
| Internet connection | Latency and bandwidth become more significant constraints. | Use only when the capacity benefit outweighs communication cost. |
A hive does not automatically make every model faster. Its primary advantage is making a larger model possible across hardware you already control.
Hive Mind vs multi-agent systems
| Approach | What is distributed? | What the system produces |
|---|---|---|
| Hive Mind | Layers or slices of one model run across several devices. | One model inference from one combined Neuron. |
| Multi-agent system | Separate agents take roles, use tools, or delegate tasks. | Coordinated work from several agent loops. |
| Independent model providers | Each device hosts a complete model or capability. | Separate providers selected task by task. |
A multi-agent workflow can use a hive as its model backend. The agent architecture and the model-serving architecture solve different problems.
Privacy and trust boundaries
Hive members process portions of the same inference, so every participating machine belongs inside the workload’s trust boundary. Use devices you control or deliberately trust, protect their operating systems and accounts, and remove machines that are lost or compromised.
When a hive serves a Local AGI Private Network, communication between approved nodes is end-to-end encrypted. Internet connectivity may still use Local AGI relay infrastructure to cross NAT boundaries; the relay routes encrypted traffic rather than running the model. Read the full private-network architecture guide for that distinction.
When should you build a hive?
Hive Mind is a practical fit when:
- a useful model does not fit on your largest single device;
- several trusted machines have memory or compute available;
- the devices can communicate over a fast, reliable connection;
- running on hardware you control matters more than minimum setup complexity; and
- your workflow benefits enough from the larger model to justify network overhead.
A single-device setup remains simpler when the model already fits, portability matters, or the connection between machines is the bottleneck.
Turn separate computers into one model provider
Start with Local AGI on the devices you trust, then combine their capacity when one machine is not enough.