What Is Ollama?

What does Ollama do?

Ollama is a runtime and model-management layer for working with AI models from your own machine. Its documentation covers Windows, macOS, Linux and Docker, along with a CLI, local API, model configuration and integrations. Instead of manually wiring together model files and inference commands for every experiment, you can use Ollama as the layer that downloads/manages supported models and exposes them to local applications. It is useful to distinguish Ollama from the model itself. **Ollama is the runtime/tooling; the model provides the learned capabilities.**

The basic workflow

The CLI is designed around simple model operations. A typical interaction looks like: Ollama's current documentation also includes commands and API endpoints for pulling models, listing installed models, showing model details, copying and deleting models. This makes it suitable both for interactive terminal use and as infrastructure behind another application.

Ollama as a local API

Ollama exposes an HTTP API for generation, chat, embeddings and model management. The documentation also lists compatibility layers for OpenAI-style and Anthropic-style APIs. That changes how you can architect a local AI project: the user interface and the model runtime do not need to be the same program. Your application can send requests to the local Ollama service much as it would send requests to a remote model API.

Models, GGUF and importing

Ollama maintains its own model workflow, but its documentation also includes importing models. GGUF matters here because it is a common local-inference format and is central to the llama.cpp ecosystem. Do not assume that any arbitrary model file will work. Compatibility depends on the model architecture, format and the capabilities of the current runtime version. Check the model/runtime documentation before downloading very large files.

Hardware and context length

Ollama can use supported hardware acceleration and publishes a dedicated hardware-support reference. Hardware support evolves, so verify your exact GPU and operating system against the current documentation. Context length is another practical setting. A larger context lets the model work with more token history, but it also increases memory requirements. This is why E—DOCEO treats context as part of VRAM planning rather than only as a model specification.

Measured behavior: context can change offload

In E—DOCEO Lab #002, we ran Qwen3 4B Q4_K_M with Ollama 0.34.2 on a GTX 1060 3GB. At active context 4096, `ollama ps` reported a 3.5 GB runtime and 57%/43% CPU/GPU placement. Reducing context to 2048 and 1024 reduced the reported runtime to 3.2 GB and 3.1 GB and increased the reported GPU share to 45% and 47%. This is a measured example, not a guarantee for other hardware or Ollama versions. It illustrates why `ollama ps` is useful when diagnosing local inference: it exposes the **active context, runtime size and processor placement** instead of leaving you to infer fit from the downloaded model size alone. [See the full Qwen3 4B context/offload experiment →](/lab/qwen3-4b-gtx-1060-3gb-context-offload/)

When is Ollama a good starting point?

Ollama is a practical starting point when your goal is to **run and integrate models**, rather than learn every low-level detail of an inference engine first. llama.cpp is worth studying alongside it when you want a deeper view of GGUF, quantization, backends and lower-level inference controls. The two topics overlap, but they answer different learning needs. E—DOCEO has now reproduced the Windows installation flow and local API inference on the documented Lab machine. See the [verified Windows installation guide](/tutorials/install-ollama-windows/) and the [DOCEO LAB](/lab/) for measured model tests. ### Sources - [Ollama documentation](https://docs.ollama.com/) - [Ollama documentation index](https://docs.ollama.com/llms.txt) - [Ollama API introduction](https://docs.ollama.com/api/introduction) - [Ollama hardware support](https://docs.ollama.com/gpu) - [Ollama context length](https://docs.ollama.com/context-length) - [llama.cpp — models and GGUF](https://github.com/ggml-org/llama.cpp/blob/master/docs/models.md)