How it works
Local inference is running an AI model on a computer, server or device controlled by the user, instead of sending each request to a remote service. The already trained model is loaded by the runtime software, which turns the input into a response using the resources available on the machine.
To do this, you need to choose a compatible model, download it or make it available locally and start a runtime that can load it. Memory, processor, graphics card and weight format influence which models can run and with what performance. Quantized versions reduce the size and numerical precision of the weights to make execution easier on limited hardware.
The application can talk to the runtime through a local interface, for example an API accessible only on the machine itself. This allows integrating the model into a prototype without the interface needing to know internal inference details.
Running locally does not mean the whole system is isolated. An application can make external calls to download models, query services or send telemetry. It is necessary to check the configuration, permissions and network traffic of the solution used.
Practical example
A developer wants to summarize documentation files without sending the content to a public service. She installs a local runtime, selects a model that fits the available memory and tests generation with a small set of non-sensitive files.
The application sends the text to the runtime's local endpoint and receives the summary. Before using this in production, the developer evaluates quality, response time, memory consumption and behavior with long documents. She also confirms that the program does not transmit content outside without authorization.
If the computer does not have enough resources, she can choose a smaller model, use quantization or run inference on a private server. Each option has different trade-offs between quality, speed, operational cost and data control.
What it's for
Local inference is useful for prototypes, offline applications, development, experiments and scenarios where data control is important. It can also reduce dependency on a remote provider and allow a team to test models without immediately integrating an external service.
Limitations include hardware requirements, runtime maintenance, model updates, monitoring and support for multiple people at the same time. On modest machines, responses can be slower or the available model can be smaller than cloud hosted options.
Privacy depends on the complete flow, not just the model's execution location. Data can appear in logs, temporary files or external integrations. Define what information enters, how long it is kept and who can access the machine.
Difference between local inference and cloud inference
With local inference, processing occurs on infrastructure that the team controls directly. With cloud inference, a provider runs the model on its servers and the application connects over the network.
The choice is not universal. A product can combine both approaches: sensitive or offline tasks stay on the device, while requests that require greater capacity go to a remote service, according to explicit rules.