Private / Local Models
Our private inference layer allows selected workloads to execute inside controlled infrastructure rather than requiring every inference request to leave the environment.
When private execution is right
We use local models where privacy, volume, latency, cost, or task specialization makes private execution the better operating choice.
Local does not mean universally superior. The central question remains whether the model and deployment path meet the workload’s required quality threshold.
Appropriate workload classes
Private and local inference is often best suited to tasks such as:
- Repeated extraction
- Classification
- Structured transformation
- Semantic processing
- Routine interpretation
- High-volume monitoring
- Narrow specialized workloads
These tasks can create substantial inference volume without necessarily benefiting from the strongest available general-purpose reasoning.
Keeping them local can reduce external inference cost, avoid unnecessary network dependence, and provide tighter control over the model runtime and data path.
Serving requirements
Private inference creates its own production surface. The system must manage:
- Model runtime
- GPU / memory capacity
- Batching
- Concurrency
- Versioning
- Monitoring
- Uptime
- API access
- Inference optimization
Model size, context length, quantization, batch size, and concurrent requests all compete for finite VRAM and compute.
A deployment that can load a model is not automatically a deployment that can serve it reliably under production load.
Hybrid architecture
Private inference exists alongside, not instead of, frontier inference.
A narrow workload may run locally while a difficult investigation is routed externally to a stronger reasoning model. Deterministic software may handle another part of the same workflow without a model at all.
Use the simplest, most reliable, and most economically appropriate tool that can do the job well.
The local model is therefore not the product. It is an execution resource selected when controlled inference produces the right combination of quality, privacy, latency, and economics for the task.
Scope
This page is the deployment policy question: which workloads should execute inside controlled infrastructure rather than leaving the environment, and what that buys in privacy, latency and unit economics. It is a decision about where inference happens, not about which model is used.
Which models are legally and practically available to run that way is Open-Weight Models. The engineering of actually serving them — runtime, memory, batching, concurrency, health — is Local Model Serving in Infrastructure.