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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →When a language model runs through a local inference path, your writing can be processed on your device rather than sent to a hosted model. That does not mean the entire app is offline or that every network feature is disabled: model downloads, updates, optional cloud services, and network-exposed local servers can create separate data paths. What happens depends on the app and its settings.
What happens to your writing during local inference?
A local LLM setup has several stages: the app prepares your message, a runtime loads the model, the model generates a response, and the app displays or routes that result. In a genuinely local inference path, the prompt goes to a runtime on your machine—not to a hosted model endpoint.
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1. The app formats your message
A chat app usually combines your latest message with conversation context and formats it for the selected model. It may add a model-specific chat template or special tokens. A tokenizer then converts the formatted input into model-ready pieces, commonly token IDs. Tokens are not necessarily whole words: one word may become multiple tokens, while a token may include only part of a word. The tokenizer is specific to the model. Hugging Face describes the role simply: “A tokenizer is in charge of preparing the inputs for a model.” (Hugging Face Transformers documentation.)
2. The runtime loads the model
The inference runtime needs access to the model’s weights. In LM Studio, for example, the documented workflow is to download model weights before running a model. The llama.cpp project uses GGUF, a format that packages model weights, tokenizer information, and metadata in a file. The specific model format must be supported by the runtime you choose.
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3. The computer evaluates the input
The runtime uses available memory and computing resources—typically a CPU, GPU, or a combination—to run inference. Support depends on the software, hardware, and model. llama.cpp lists CPU and accelerator backends, quantized inference, and hybrid CPU/GPU inference for models that do not fit entirely in available video memory. Quantization can reduce memory use, but it does not establish a universal speed or quality result: outcomes depend on the model and machine.
4. The model generates text token by token
A generative model predicts a next token from the input and the text generated so far. A decoding step selects a token from the model’s output distribution; the process repeats until the model reaches an end condition or a length limit. The tokenizer converts generated token IDs back into displayable text. Hugging Face’s text-generation documentation explains that a model generates the next token from the prompt and its own prior outputs, continuing until a predefined length or an end-of-sequence token. (Hugging Face Transformers documentation.)
5. The app displays or routes the result
The app presents the response, or passes it to another feature. If that feature sends content to a hosted service, it is a separate path from local inference. A local server can also be configured to accept requests from other devices; “running on my computer” does not, by itself, mean “only accessible by me.”
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Does a local LLM send prompts over the internet?
Not necessarily. If the selected model is running through the app’s local inference path, prompt processing can remain on your device. But whether an app makes other network requests is a product- and configuration-specific question. “Local” describes where a particular inference happens; it is not a blanket guarantee that the app has no internet activity, telemetry, cloud features, or network access.
LM Studio: local chat and offline operation
LM Studio says that after model files are on the machine, local chats and document chat (RAG) can work offline, and that document processing happens locally. It says content entered in local chats does not leave the device. The same documentation identifies internet-dependent operations, including model search and downloads, runtime downloads, and app update checks. These are LM Studio’s documented behaviors, not an independent audit of every installation or extension. (LM Studio: Offline Operation.)
Ollama: distinguish local processing from cloud features
Ollama’s privacy policy, last updated March 2026, states: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” The policy separately describes cloud-hosted models, where prompts and responses are processed transiently, and says Ollama may collect limited device and usage metadata, such as app version, request counts, IP address, or model-download metadata. The statement about locally processed content should not be mistaken for a claim about cloud-model requests or all service metadata. (Ollama Privacy Policy.)
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When can a local AI app use the internet?
Some connectivity is needed to find or obtain model files and software. Other connections depend on optional features or configuration. Check the actual path used for the task rather than inferring it from an app’s “local” label.
- Before use: Model discovery, model downloads, runtime downloads, and app updates may require internet access. LM Studio lists these among its network-dependent operations.
- During use: Hosted models and web search are cloud-connected features. Ollama documents cloud models and web search separately from local inference.
- After setup: Offline use is possible for supported local workflows once the necessary files are available. LM Studio documents offline local chat, document chat, and local serving.
- For other devices: A local inference server may be reachable over a network if its bind address or network configuration allows it.
For Ollama, its FAQ documents a local-only mode that disables cloud features, including cloud models and web search. It also says the server binds to 127.0.0.1 by default; users can change the bind address, and the documentation describes proxy and tunnel configurations. LM Studio documents local server use on localhost or a local network. A localhost-only setup and a server exposed to a network have different access boundaries. (Ollama FAQ; LM Studio local server documentation.)
How to check whether your workflow stays local
- Confirm the selected model and route. Check that the app is using a model installed for its local runtime, not a cloud-hosted model or another remote provider.
- Review optional features. Look for cloud models, web search, remote APIs, plugins, or other integrations that could handle content outside the local inference path. In Ollama, the documented local-only mode disables cloud features.
- Separate setup traffic from prompt processing. Model searches, downloads, runtime installation, and update checks can require connectivity even when a later local chat works offline.
- Check server exposure. If you have enabled an API or local server, inspect its bind address and any proxy or tunnel configuration. A server bound to localhost is not the same as one configured for network access.
- Use offline operation when appropriate. After obtaining the required model and runtime files, disconnecting from the internet can help confirm whether a supported local workflow still operates. This does not replace checking the app’s configuration or establish what it may do when connectivity returns.
What affects whether a local model runs well?
There is no single hardware specification that guarantees a good experience for every model. Fit and responsiveness depend on the model, its format and context, the runtime, available memory, and whether requests run concurrently. llama.cpp documents quantized inference and CPU/GPU hybrid execution; Ollama’s FAQ discusses memory-dependent model loading and parallelism. Those capabilities are not a universal benchmark or a promise that a particular model will run at a particular speed on a particular computer.
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Runtime compatibility matters as much as the app interface. LM Studio documents llama.cpp with GGUF across Mac, Windows, and Linux, as well as MLX support on Apple Silicon. Check the selected model’s format and the runtime available for your operating system before downloading it. (LM Studio documentation overview; llama.cpp documentation.)
Is local AI private?
Local inference can keep prompt processing on your device, but “local AI” is not a universal privacy certification. Product statements describe particular products and workflows; they do not prove that every app, plugin, extension, or installation has been independently audited. For sensitive writing, verify the model route, optional cloud features, and server exposure in the software you actually use. Also distinguish content from service metadata: a provider may make a product-specific statement about locally processed prompts while separately describing metadata or cloud processing.
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