Private and hybrid execution

Self-Hosted AI Coding Infrastructure for Autonomous Development

Can Nexus use self-hosted AI coding models?

Nexus is designed to route suitable engineering work to approved self-hosted or private models while preserving the same project state, evidence and governance layer. A deployment can use hybrid model routing; this does not mean every Nexus component or every model must run fully on-premise.

One governance layer across model locations

Private AI software development often needs different execution choices for different work. Routine review or bounded coding tasks may suit local LLM coding agents, while other tasks may use an approved hosted model.

Nexus keeps repository identity, work ownership and evidence rules consistent across those routes. Model location does not change which worker owns a slice or which gate authorises integration.

Approved model routing
Shared evidence contract
Portable customer-owned repositories
Consistent release authority

GPU capacity is an engineering constraint

Self-hosted coding agents depend on model fit, context requirements, queueing and GPU utilisation. The useful comparison is cost per verified delivery, not the nominal price of a token or GPU hour.

Nexus capacity controls are intended to route work deliberately and avoid turning local infrastructure into an unbounded queue. The GPU economics resource explains the variables without promising that local execution is always cheaper.

Model and context fit
Throughput and queue latency
Power and operations
Review yield per delivery

Private does not remove the need for controls

Running an agent on private infrastructure does not make its changes correct. Fenced execution, exact-SHA review, regression evidence and human release gates remain necessary.

Enterprise deployment requirements vary. Nexus does not imply universal on-premise availability; deployment and routing boundaries should be confirmed for the required environment.

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