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A local large language model (local LLM) is a language model whose inference—the work of processing a prompt and generating a response—runs on hardware you control, such as your computer or an edge device, rather than entirely on a remote provider’s servers. “Local” describes where that computation happens; it does not automatically mean the model is open-source, freely licensed, or that every part of the app stays on your device.
What “local” means for an LLM
When you submit a prompt, inference is the computation that turns it into a response. If your computer or another system under your control performs that computation, the model is running locally. A model download alone does not prove this: an app can present a local-looking interface while sending requests to a remote service.
Local inference can run on a CPU or use a GPU, depending on the software, model, and available hardware. Small models can also be designed for edge devices. For example, Ollama describes its Llama 3.2 1B and 3B text-only variants as optimized for mobile or edge use. That description does not mean every small model supports every task, or that its responses match those of a larger model. Ollama’s announcement about Llama 3.2 gives details on those variants.
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Local LLM versus cloud LLM
The key distinction is where the model processes the prompt and generates the response. A cloud model uses the provider’s computing infrastructure; a local model uses hardware controlled by the user. Some products offer both, so the product name or interface is not enough to tell you which one handles a particular request.
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| Aspect | Local inference | Cloud inference |
|---|---|---|
| Where generation runs | On user-controlled hardware, such as a computer or edge device. | On infrastructure operated by the service provider. |
| Prompt handling | A properly configured local workflow can process prompts on-device. Connected features or hybrid steps may still send content elsewhere. | The request is sent to the cloud service for processing. |
| Compute and model choice | Choices are constrained by available hardware, memory, runtime, and the workload. | The provider supplies the compute, including for models that may not fit on a personal computer. |
| Setup and control | Users may choose and run models on their own system; some tools also provide a local API. | The provider operates the model service and its infrastructure. |
Neither approach is automatically faster, cheaper, more capable, or more secure in every situation. Performance and cost depend on the model, hardware, workload, and service terms. For a hybrid tool, check which operations run locally and which send content to a provider.
Does “local” mean your prompts stay private?
Local inference can reduce data exposure when the entire workflow processes prompts on your device and does not send them to remote services. But “local” by itself is not a privacy guarantee: an app may use telemetry, remote tools, or cloud inference for some features.
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Ollama’s privacy policy, last updated in March 2026, says that content processed locally—including prompts, responses, and model interactions—is not collected, stored, transmitted, or accessible to Ollama. The policy separately says cloud-hosted models process prompt and response content transiently to provide the service. These are statements about Ollama’s described operation, not a guarantee for every local-model app. Read the policy and check the settings and connected services of the tool you use: Ollama Privacy Policy.
What hardware does a local LLM need?
Requirements vary with the model and workload. Memory figures for one model are not universal minimums for local LLMs. As a specific example, Ollama’s August 2023 Code Llama guide lists 16 GB or more of memory for the 13B variant and 32 GB or more for the 34B variant. Those recommendations apply to the named Code Llama versions, not every model or workload. Ollama’s Code Llama guide provides the variant-specific details.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 64GB pool, which is perfect for running LLMs such as Deepseek 32B, which runs comfortably on this machine.
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If you are considering a 32GB RAM laptop for local LLM use, treat that as a hardware category to investigate, not a guarantee that it will run every 34B model well. Before buying, check the requirements for your intended model, its quantization, context length, and runtime. The cited Code Llama guide does not establish that 32 GB is sufficient for every 34B model or workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is a local LLM the same as an open-source model?
No. “Local” describes where inference runs. “Open-source” and “open-weight” describe aspects of a model’s availability and licensing; they do not tell you where inference happens. A model can run locally without being open-source, and an openly available model can be used through a cloud service. Check the model’s license and the software’s data flow separately.
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How to tell whether an LLM is actually local
- Find out whether prompts and generation run on your computer or on a provider’s server.
- Check whether features such as web search, file processing, or voice input use remote services.
- Look for a cloud or hybrid mode, and determine which content it sends remotely.
- Review the app’s privacy policy and network or telemetry settings rather than relying on the word “local.”
For examples of the distinction, Ollama documents local CPU and GPU execution as well as cloud models, which use provider-hosted compute. See its local execution overview and cloud models explanation.
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