When you normally use ChatGPT, Claude, or Gemini, you're mostly calling the model in the cloud. The user's question is sent to the provider's servers, and the cloud model computes and returns the answer. A local model is a different idea: running the model on your own computer or server.
Ollama is currently one of the popular tools for running local large models. Its value isn't inventing the concept of a "local model," but lowering the barrier to downloading, running, and managing local models, making it easier for ordinary developers and tinkering users to run an LLM on their own machine.
Grab It in One Sentence First
A local model is like installing an AI assistant inside your own computer; Ollama is like a tool that makes it easier for you to download, run, and manage local models.
Its biggest difference from a cloud model is that the model computation happens on your own machine or a server you control, rather than sending the request to an external model service every time.
Why Local Models Are Popular
Both Ollama's official site and its GitHub project emphasize running large language models on your own machine. Many introductions also mention that it lets users download and start models with relatively simple commands, and call them through a local interface. For developers, this makes experiments, prototypes, and internal tools easier to set up.
The appeal of local models is, first, that the data doesn't necessarily have to be sent to the cloud. For content like notes, drafts, internal documents, and code experiments, running locally gives people a greater sense of control. Second, when offline or on an unstable network, a local model may still be usable. Furthermore, for long-term, high-volume, fixed-scenario use, the cost structure between local compute and cloud calls also differs.
flowchart LR
User["User"] --> Local["Local computer / server"]
Local --> Ollama["Ollama"]
Ollama --> Model["Local model"]
Model --> Ollama
Ollama --> UserIt's Not a Simple Substitute for Cloud Models
A local model has more freedom, but its limits are also more direct. The larger the model, the more memory, VRAM, disk, and cooling it needs. A small computer can run small models, but that doesn't mean it can smoothly run every strong model. A cloud model usually has more powerful hardware, more mature services, and continuous updates behind it, while a local model relies more on your device's configuration and your maintenance ability.
Local doesn't equal absolute security either. If you expose a local model service to the public internet, or let untrusted applications call it freely, it brings risk all the same. Data not leaving the computer is just one advantage; configuration and permissions still need to be handled carefully.
What Situations Ollama Suits
Ollama is well suited to learning, experimentation, offline drafts, internal prototypes, and development testing. For example, if you want to compare the answers of different open-source models, build a small document-Q&A tool locally, or connect your code editor to a local model, you can start with it.
But if you need the strongest reasoning capability, a stable SLA, enterprise-grade permission management, or high concurrency, a cloud model or an enterprise deployment solution may be more suitable.
Where It's Easy to Misunderstand
The most common misconception is thinking a local model is necessarily stronger, more secure, and cheaper. In fact, how strong a local model is depends on the model and hardware; how secure it is depends on the configuration and data flow; and how cheap it is depends on usage volume, electricity cost, device cost, and maintenance cost.
Another misconception is treating Ollama as the model itself. Ollama is a tool for running and managing models; what actually answers the questions is the specific model you download and run.
How to Decide Whether to Use It
If you value local control, want to learn open-source models, need offline experiments, or don't want to call a cloud API for every little test, a local model is worth trying. If you care more about the strongest performance, stable service, and a maintenance-free experience, a cloud model is usually more worry-free.