How to Run Local LLMs with OpenVINO on Intel Arc

What OpenVINO is

OpenVINO is Intel’s toolkit for optimizing and deploying AI inference on Intel hardware. It is different from something like Vulkan or SYCL because it is focused specifically on inference rather than being a general GPU API.

For Intel Arc users, that makes OpenVINO another route for running local AI workloads on the GPU.

The basic stack looks more like this:

Intel Arc
   ↓
Intel runtime
   ↓
OpenVINO
   ↓
LLM application or server
   ↓
Model

Why use OpenVINO?

The main reason to use OpenVINO is that it is designed around Intel hardware. The toolkit can target Intel CPUs, integrated GPUs, and discrete GPUs through the same general inference ecosystem.

That can make it particularly interesting if you have a system where the CPU and Arc GPU are both important parts of the workload.

OpenVINO Model Server

OpenVINO Model Server (OVMS) takes the idea further by providing a model-serving system rather than just a local inference library.

That makes it useful when you want to expose an inference endpoint to another application instead of sitting at a terminal and generating text manually.

For example, you could have:

Application
    ↓
API
    ↓
OVMS
    ↓
OpenVINO
    ↓
Intel Arc

This is a different use case from a desktop application such as LM Studio. OVMS is much more interesting when you are building your own AI service or integrating inference into another application.

OpenArc and other projects

You may also run into projects built around OpenVINO that make local LLM inference easier to use on Intel GPUs. OpenArc is one example of an Intel-focused local inference project.

The exact capabilities and supported models of these projects change quickly, so treat the project documentation as the source for current installation instructions.

Model formats

OpenVINO does not revolve around exactly the same model ecosystem as llama.cpp. Depending on the application, model conversion or a particular supported model format may be involved.

That is one of the biggest differences between choosing OpenVINO and choosing something like llama.cpp with GGUF models.

If you already have a large collection of GGUF models, llama.cpp may be the simpler starting point. If you are building an Intel-oriented inference system, OpenVINO becomes much more interesting.

Arc GPU support

OpenVINO can use Intel GPU devices for supported inference workloads. The exact device and model support depends on the OpenVINO version and the model/runtime combination you are using.

When troubleshooting, first make sure OpenVINO can see the Intel GPU before worrying about the LLM application on top of it.

OpenVINO provides device-selection mechanisms so an application can target CPU or GPU. A typical OpenVINO application will expose a device choice such as:

CPU
GPU
AUTO

The exact syntax depends on the API or application being used.

When OpenVINO makes sense

OpenVINO is worth looking at if you:

• Want an Intel-focused inference stack.
• Want to use Intel CPU and GPU hardware together.
• Are building an application around an inference API.
• Want to use OVMS for model serving.
• Are interested in Intel’s optimization and deployment tooling.

If you mainly want to download a GGUF and chat with it, llama.cpp, Ollama, or LM Studio may be a simpler starting point.

For the other GPU backends, see How to Run llama.cpp on Intel Arc with SYCL and How to Run Local LLMs on Intel Arc with Vulkan.