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GenAI Studio is a containerized solution to enable rapid prototyping and customization by simplifying the creation and deployment of Generative AI applications on Qualcomm Linux systems. GenAI Studio provides OpenAI-compatible APIs for the following core services: text generation, text-to-speech, image generation, image-to-text (VLM), and speech-to-text. All services are accessible through a web interface and OpenAI-compatible API endpoints.
  • GenAI Studio doesn’t include model files or container images.
  • You must generate the models and build container images.
  • Ensure your host computer meets the requirements.
  • Use the host computer to generate models.
  • Use the target device to build the docker containers.

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High-level architecture

The following diagram shows the high-level architecture of GenAI Studio. Each functional block runs as an independent container, providing isolation, scalability, and extensibility. This allows new modalities to be added as additional containers without disrupting existing services.
Each endpoint has a specific port. Note the port number in endpoint port numbers.
High-level architecture of GenAI Studio showing containerized use cases and backend interactions The following table lists each endpoint and its associated port number. Endpoint port numbers

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User interaction

The user accesses GenAI Studio through a web page hosted on the host computer or through API calls from third-party applications with OpenAI-compatible endpoints.

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Request routing to target

The webpage or API call communicates with the orchestrator service running on the target device (for example Qualcomm Dragonwing™ IQ9) and converts each user action on the UI into a corresponding REST API call for the backend services.

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Orchestration layer

The orchestrator container acts as the central hub. It receives requests, manages session history, handles multi-turn chat continuity (KV-cache), and aggregates responses from individual modality containers to present a unified experience to the user. Containerized architecture and scalability: Each functional block (orchestrator, text-to-text, text-to-speech, text-to-image, image-to-text, speech-to-text) runs as an independent container. This provides the following:
  • Isolation between services.
  • Scalability, since each service scales independently based on use case.
  • Extensibility, since you can add new modalities or models as additional containers.
See the documentation of each respective use case for more detailed information.

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Text-to-text

This application runs a text-to-text large language model (LLM) using the Genie API. It provides a persistent (always-on) LLM server that supports the following:
  • LLM response generation from user prompts (text-to-text).
  • Preloaded model reuse to avoid reloading for each request.
  • Control endpoints for model/session reset and model reload.
  • System prompt updates directly from the UI.
  • Conversation history that allows the UI to fetch previous messages.
Call flow of the GenAI studio components and how they interact with each other in a text generation use case. For more information, see the text-to-text README and the code flow files.

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Text-to-image

The following image shows an example call flow sequence in a text-to-image container. Call flow of the GenAI studio components and how they interact with each other in an application. For more information, see the text-to-image README and the code flow files.

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Image-to-text

The image-to-text container uses a vision-language model (VLM) to describe images with natural language, enabling visual understanding and moderation use cases. For more information, see the image-to-text README and the code flow files.

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Text-to-speech

The following image shows an example call flow sequence in a text-to-speech container. Call flow of the GenAI studio components and how they interact with each other in an application. For more information, see the text-to-speech README.

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Speech-to-text

The following image shows an example call flow sequence in a speech-to-text container. Call flow of the GenAI studio components and how they interact with each other in an application. For more information, see the speech-to-text README and the code flow files.

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Setup GenAI Studio

GenAI Studio is supported on IQ9 and IQ8 with Ubuntu distribution on Qualcomm IoT platforms.
Language models aren’t shipped with GenAI Studio. You must generate a model using AI Hub.
1

Download and flash the build for ubuntu from build guide

Download and flash the build for ubuntu from build guide.
2

Clone the repository to the target device and go to the local directory

3

On the host computer, prepare the SDK (optional)

This is only required if you are bringing up the full stack or a private STT or TTS service.
4

From the sample app repository root directory on the target device, complete the preflight checks

Ensure that the device is properly provisioned before running the preflight checks. For initial device provisioning, see device setup.
5

Prepare models for target

See each service’s model generation and setup documentation for more information.
Prepare the model folders under /opt/genai-studio-models.
6

Build base images (one-time setup)

Navigate to GenAI-Studio folder and run below command:
7

Build service images

See the service README files (for example, core-services/README.md) for guidance to ensure all required files are in place.
8

Start services with docker-compose

See the README for the recommended environment variables to set before running docker-compose.
9

Run service health checks and functional checks to verify all services are running

10

Run the unified test suite on Host Machine

For functional endpoint checks and testing, see functional endpoint test and the individual service README files.
11

Access Via Orchestrator

Open a brownser and run below URL:
If any service fails to collect logs, see the service’s troubleshooting and pain point documents from the service’s docs folder. To check service logs, run the following commands: