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AI Infrastructure & Serving

Openarc

Openarc is a self-hosted inference server that uses OpenVINO to serve AI models on Intel devices through OpenAI-compatible endpoints.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Openarc — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Openarc when you need a self-hosted inference service on Intel devices and your application can call OpenAI-compatible endpoints.

Who it is for

  • Developers hosting model inference on Intel hardware.
  • Teams building applications that need local language, vision-language, speech, embedding or reranking services.

Documented uses

  • Serve language and vision-language models to applications through compatible inference endpoints.
  • Host speech transcription and synthesis alongside language-model inference.
  • Provide embedding and reranking endpoints for retrieval workflows.
  • Inspect request metrics and benchmark LLM inference.
Before you choose

Requirements & limits

The project lists setup paths for Linux, Windows and Docker. Its README says the latest models currently require nightly OpenVINO GenAI wheels.

  • The server targets Intel devices and is powered by OpenVINO.
  • Endpoint availability varies by model: for example, the listed speech endpoint supports Kokoro, while transcription supports Whisper and Qwen3-ASR.
  • The project is under active development and notes a nightly-wheel requirement for the latest models.
  • The repository is licensed under Apache-2.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare an OpenArc environment on Linux

Set up the documented Linux Python environment and review the available OpenArc commands.

Before you start

  • Linux system with appropriate drivers for the OpenVINO device in use.
  • Install uv from Astral.
  • Clone the OpenArc repository.

Steps

  1. From the cloned repository, run the command below.

    Documented command
    uv sync

  2. Activate the environment with source .venv/bin/activate.

  3. Install the latest Optimum Intel OpenVINO integration with the command below.

    Documented command
    uv pip install "optimum-intel[openvino] @ git+https://github.com/huggingface/optimum-intel"

  4. Install the nightly OpenVINO GenAI wheel with the command below.

    Documented command
    uv pip install --pre -U openvino-genai --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly

  5. Run openarc --help to view the available commands.

Expected outcome

The Linux Python environment is prepared and the OpenArc command help is available.

Guide limitations

  • OpenVINO devices require different drivers; consult the OpenVINO system requirements for the device and OS.
  • The latest models currently require nightly OpenVINO GenAI wheels.
  • These steps prepare the environment; they do not document starting a serving process.
Documentation 1 ↗
Source-based practical guide

Start OpenArc with Docker Compose

Launch OpenArc using the documented Docker Compose setup.

Before you start

  • Docker Compose.
  • The OpenArc repository or its docker-compose.yaml file.
  • For model mounting, a local model directory and a configured MODEL_PATH.

Steps

  1. Obtain the repository's docker-compose.yaml file or clone the repository.

  2. Optionally set MODEL_PATH to the local model directory; the documentation maps models into /models in the container.

  3. Run docker-compose up -d.

Expected outcome

The Docker Compose configuration launches OpenArc in detached mode.

Guide limitations

  • The documentation describes changing the Compose image entry for a Battlemage-optimized image but does not include the exact edit.
  • An API key only enforces client authentication when OPENARC_API_KEY is set and --use-api-key is passed to openarc serve start.
  • The documented steps do not verify model availability or inference requests.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
OpenarcServe language and vision-language models to applications through compatible inference endpoints.Docker, Linux, Self-hosted, WindowsThe project lists setup paths for Linux, Windows and Docker. Its README says the latest models currently require nightly OpenVINO GenAI wheels.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
SearchSavior/OpenArc · Official homepage title · retained in its source language
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