Inference

As explained in Build Applications with Local AI Models on a Mac

Model inference is the process of applying a trained model to input data to produce an output.

Model inference means applying a trained model to an input to produce an output. In a voice assistant, it is the step that turns transcribed speech into an AI response. Ollama runs the language model locally, so the prompt and response can stay on your Mac. section 1.0 section 10.9

Follow one spoken request through the application:

• The browser captures audio; MLX Whisper transcribes it into text. section 10.9
• The text reaches the same message handler used for typed input. Ollama performs LLM inference and streams the response. section 10.9

Local inference can work without an internet connection after the model has been downloaded. That reduces the need to transmit prompts and responses, though privacy still depends on the rest of the application and its configuration. Inference need not run on the same device as the interface: a small agent can handle messages while an LLM API or an Ollama server on a larger machine runs the model. section 14.6 section 16.5.5

Figure 16.3 The architectural contrast between OpenClaw and PicoClaw: a full-featured agent and an ultra-lightweight agent, both connecting to LLM backends

Compare agent runtimes that connect to LLM backends rather than performing model inference themselves. section 16.5.5

How Build Applications with Local AI Models on a Mac covers it

How deep it goes

Explains inference through a working pipeline and deployment choices, without descending into model internals.

Examples

Uses a browser-captured voice request processed by MLX Whisper and Ollama, plus an agent connected to a separate LLM backend.

What it assumes

Assumes no command-line or machine-learning experience; treats basic Python familiarity as helpful when coding begins.

What you can do after

Apply it via Ollama as the response stage of a local voice assistant.

How it connects

Connects inference to speech recognition, streaming responses, offline operation, and agent architecture.

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Related concepts

• pipelinepart of · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
• Ollamauses · Ollama is software for downloading, running, and serving language models locally.
• computeuses · Compute is the processing capacity and computational resources available to or consumed by a system when executing workloads.
• PyTorchused by · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
• trainingcontrasts with · Training is a process of developing or improving a person’s or model’s capabilities through instruction, experience, or adjustment using data.

This explainer was generated by Manning's knowledge graph from the text of Build Applications with Local AI Models on a Mac. The section links point to the parts of the book it draws on.