Why would you run a local LLM?

For privacy, since data stays on your hardware; for cost control, since there is no per-token fee; for offline use; and for customisation, since you can choose, tune, and configure models freely. The trade-off is limited model size and the work of managing hardware and software.

There are no per-token charges, and many open-weight models are free to download under their licences. But you pay for hardware, electricity, and your own time maintaining the setup. For heavy, repetitive use, that can be cheaper than API fees; for light use, often not.

Are local LLMs as good as ChatGPT or Claude?

Usually not at the very top end, because the largest frontier models are not available to run locally and local hardware limits size. For many everyday tasks, good local models are very capable, especially when given relevant context through retrieval.

What Is a Local LLM?

A local LLM is a large language model that runs on hardware you control, such as a laptop, workstation, or private server, rather than through a provider's cloud API. Open-weight models are downloaded and run with tools such as Ollama, LM Studio, or llama.cpp. People run them for privacy, predictable cost, offline use, and customisation.

What makes an LLM local

A model is local when inference runs on infrastructure you control. You download the model weights, load them with a runtime, and send prompts to it directly or through a local API. No prompt or response needs to leave the machine. This is possible because many capable models are released with open weights under various licences.

Why people run local LLMs

Privacy is the most common reason: source code, medical notes, legal documents, and personal data can stay on the device. Cost is another: heavy, repetitive use has no per-token bill. Offline availability matters for travel, secure environments, and unreliable connections. Local models can also be fine-tuned or customised freely within their licences.

What local LLMs are used for

Common uses include coding assistance, summarising and searching personal notes, drafting and editing text, classification and extraction over private documents, local agents that automate tasks, and experimentation with prompts and fine-tuning. Many people pair a local model with a retrieval index over their own files.

What you need

You need a runtime, such as Ollama, LM Studio, llama.cpp, or vLLM, and hardware with enough memory for the model. GPU VRAM or unified memory determines which model sizes fit, and memory bandwidth determines speed. Quantised models reduce memory needs substantially.

Limits of local LLMs

The largest frontier models are not available to run locally, and local hardware limits model size and context length. Local models may be weaker at complex reasoning, long agentic tasks, and broad knowledge. You also take on maintenance: updates, model choice, performance tuning, and security of the machine.

Local versus cloud

Cloud models offer the highest capability, large contexts, and no hardware management, priced per token. Local models offer privacy, fixed costs, and control, with capability limited by hardware. Many teams use both: local for private and routine tasks, cloud for the hardest work.

Making local models more useful

Smaller models answer better when given precise context. Indexing your notes and code, then retrieving relevant chunks before each prompt, gives a local model the facts it lacks. RDK provides that retrieval layer for vaults and codebases, with encrypted private chunks, so local and hosted models alike call the LLM only when retrieval has no answer.

Is a local LLM right for you?

Consider one if privacy requirements are strict, usage is high and repetitive, you need offline access, or you want full control over the model. Stay with cloud models if you need maximum capability, very long contexts, or do not want to manage hardware.

Popular ways to run one

Ollama runs models from the command line with a local API. LM Studio offers a desktop app with a model browser and chat interface. llama.cpp is a lightweight engine that runs on CPUs and GPUs and powers many other tools. vLLM targets high-throughput serving on GPUs. Most can expose an OpenAI-compatible endpoint, so existing applications can switch to a local model by changing a base URL.

Licences and model choice

Open-weight models come with different licences. Some allow broad commercial use; others restrict certain uses or scale. Check the licence before building on a model. Choose models by task: coding-tuned models for code, instruction-tuned models for chat, and small specialised models for extraction or classification.

Security considerations

A local model is only as private as the machine it runs on. Keep the runtime updated, restrict network exposure of local servers, and be careful with plugins or tools that connect the model to files and the internet. Treat model files from unknown sources with caution.

Running one locally

Install a runner such as Ollama or LM Studio, download a small instruction-tuned model, and try it on everyday tasks. Then move to larger or specialised models as your needs and hardware allow. Adding a retrieval index over your notes or code is often the biggest single improvement.

Local models in teams

Teams can run a shared local model server on private infrastructure, so data stays inside the organisation while many people use it. This needs capacity planning, access control, and monitoring, much like any internal service.

Local LLMs and agents

Local models can power agents that read files, call tools, and automate tasks on your machine. They work best on short, well-defined tasks with clear tools. For long multi-step work, keep humans reviewing actions and give the agent retrieval so it does not have to remember everything in context.

Frequently asked questions

Why would you run a local LLM?: For privacy, since data stays on your hardware; for cost control, since there is no per-token fee; for offline use; and for customisation, since you can choose, tune, and configure models freely. The trade-off is limited model size and the work of managing hardware and software.
Is a local LLM free?: There are no per-token charges, and many open-weight models are free to download under their licences. But you pay for hardware, electricity, and your own time maintaining the setup. For heavy, repetitive use, that can be cheaper than API fees; for light use, often not.
Are local LLMs as good as ChatGPT or Claude?: Usually not at the very top end, because the largest frontier models are not available to run locally and local hardware limits size. For many everyday tasks, good local models are very capable, especially when given relevant context through retrieval.