A local large language model (local LLM) is a language model whose inference—the processing of a prompt and generation of a response—runs on hardware controlled by the user, such as a computer or edge device, rather than entirely on a remote provider’s servers. “Local” describes where inference happens; it does not, by itself, mean the model is open-source, freely licensed, or that every part of the app stays on your device.
What does “local” mean for an LLM?
Inference is the computation that turns a prompt into a model response. If that computation takes place on your own computer or another device or infrastructure you control, the model is running locally. A model’s files may be downloaded to your device, but that alone does not prove that its inference is local: an application could still send prompts to a remote service.
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Local execution can use a computer’s CPU or GPU, and small models can be designed for mobile or edge devices. Ollama’s examples illustrate these arrangements, but they are vendor documentation rather than an independent technical standard. See Ollama’s CPU and GPU examples and its description of small models for mobile and edge devices.
How is a local LLM different from a cloud LLM?
| Question | Local inference | Cloud inference |
|---|---|---|
| Where does the model process the prompt? | On user-controlled hardware, such as a computer or edge device. | On infrastructure operated by a service provider. |
| Where does the prompt go? | It can remain on the device in a properly configured local workflow. Connected features or hybrid steps may still send content elsewhere. | The request is sent to the cloud service for processing. |
| What hardware runs the model? | The user supplies suitable compute and memory, which constrain model choice and workload. | The provider supplies compute, including for models too large to run on a personal computer. |
| What setup and control are involved? | Users can choose and run models on their systems; some tools also provide a local API. | The provider operates the inference service. A familiar software interface does not necessarily mean inference is local. |
Ollama documents local and cloud models, including cloud-hosted inference using provider compute. Its cloud models overview describes that distinction. Neither approach is automatically faster, cheaper, more capable, or more secure in every situation; the outcome depends on the model, hardware, workload, and service terms.
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Does “local” mean the model is open or private?
No. “Local” identifies where inference runs. It is not a synonym for “open-source” or “open-weight,” and it does not establish the model’s license. Those are separate questions: check the model’s license and terms independently.
Local processing can reduce data exposure when the entire workflow stays on the device, but the label alone is not a privacy guarantee. Ollama’s privacy policy, last updated in March 2026, says it does not collect, store, transmit, or access prompts, responses, model interactions, or other content processed locally through its service. The policy separately says that cloud-hosted models process prompt and response content transiently to provide the service. These statements describe Ollama’s stated practices, not every local LLM application. Read Ollama’s privacy policy and check whether the specific app uses telemetry, remote tools, or cloud fallback.
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What hardware does running a local LLM require?
Requirements vary with model size and workload. As a specific example, Ollama’s August 2023 guide lists 16 GB or more of memory for Code Llama 13B and 32 GB or more for Code Llama 34B. Those are vendor recommendations for those variants, not universal minimums for all local models or a guarantee that a particular workload will fit. The guide is at Ollama’s Code Llama local-running guide.
Ollama describes Llama 3.2’s 1B and 3B text-only variants as optimized for mobile or edge devices. Smaller models can make local use practical on more constrained hardware, but capability and supported tasks depend on a model’s design and size. See Ollama’s Llama 3.2 announcement.
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If you are considering a “32GB RAM laptop for local LLM” as a hardware category, treat that as a starting point for checking a particular model—not a tested recommendation or a universal buying rule. Before choosing a computer, check the requirements for your intended model, quantization, context length, and runtime. Ollama’s 2023 Code Llama guide supports only the model-specific memory examples above.
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How can you tell whether an app is actually running a model locally?
- Check where inference is performed. Look for an explicit description of local execution, not just language such as “download,” “desktop,” or “on-device.”
- Inspect connected features. Search the app’s privacy policy and settings for cloud models, remote tools, telemetry, and fallback behavior.
- Check available model and hardware details. Local model selection is constrained by device compute and memory; an app that offers both local and cloud models may handle different requests in different places.
- Look for local controls or interfaces. Some tools expose a local API or acceleration options. Ollama’s Windows preview announcement describes local API and acceleration examples, but the presence of a local interface alone does not prove every workflow remains local.
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