The Livepeer AI network is in its Beta phase. Bugs or issues may be encountered. Contributions to improvement are appreciated - please report problems via the issue tracker. Feedback is invaluable for enhancing the Livepeer AI network. Thank you for your contributions!
Livepeer AI, also known as the Livepeer AI (Video) Network, is the first step toward bringing powerful AI video capabilities into the Livepeer network. It enables video developers to add a rapidly growing suite of generative AI features such as text-to-image, image-to-image, image-to-video, and upscaling to their applications. Livepeer Node operators are able to earn revenue by deploying their GPU resources for AI processing tasks. Ready to dive in? Choose one of the cards below to kickstart your journey with the AI Subnet.

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Kickstart Your Journey

AI Pipelines

Explore the available AI pipelines.

Set up AI Orchestrator

Set up your AI Orchestrator node and earn fees from AI tasks.

Set up AI Gateway

Establish an AI Gateway to serve a broad customer base with AI tasks.

Build Your App

Develop your innovative app on the AI Subnet efficiently and at scale.

Showcase

Discover projects built on the AI Subnet.

AI SDKs

Explore developer SDKs for interacting with the Livepeer AI API.

AI Gateways

Choose an AI Gateway to interact with the AI Subnet.

AI API Reference

Explore the AI Subnet API to integrate AI capabilities into your projects.

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Background on Livepeer AI

Livepeer AI, initially proposed in this SPE treasury proposal, represents a significant evolution within the Livepeer ecosystem. This decentralized, open-source framework seamlessly integrates a variety of generative image and video AI inference tasks into the existing Livepeer network infrastructure. These enhancements strengthen Livepeer’s transcoding services, celebrated for their low cost and high reliability, while also paving the way for groundbreaking applications across both emerging Web3 environments and established Web2 sectors. Designed to revolutionize creative processes, Livepeer AI reflects Livepeer’s commitment to creating a globally accessible open video infrastructure. By striving to democratize AI, Livepeer AI empowers users to leverage any form of image and video compute with advanced AI tools. By equipping video applications with these tools and reducing reliance on centralized computing resources, we are dedicated to extending cutting-edge AI capabilities to a broader audience, fostering a more equitable digital landscape. Additionally, we are committed to supporting open-source research by funding researchers who develop in the open, further driving innovation and collaboration within the community.

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Advantages of Livepeer AI

  • Decentralization: Enhances security and resilience by eliminating single points of failure.
  • Cost-Effectiveness: Provides AI inference capabilities at a significantly lower cost compared to traditional cloud services.
  • Scalability: Easily scales in response to user demand, ensuring reliable service without interruptions.
  • Open-Source Innovation: Encourages collaborative development, accelerating innovation and broadening the range of supported applications.

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How It Works

Livepeer AI, built on the established Livepeer network, leverages a decentralized payment infrastructure for compensating AI orchestrator nodes for performing AI inference tasks. The network consists of two primary actors:
  • AI Gateway Nodes: These nodes manage the flow of AI tasks from applications and users, directing them to the appropriate Orchestrator nodes based on capability and current load, ensuring efficient task allocation and system scalability.
  • AI Orchestrator Nodes: These nodes manage the execution of AI tasks. They can perform AI inference tasks on GPUs located on the same machine or be connected to multiple machines with multiple GPUs, known as AI Worker Nodes. The orchestrator or its worker nodes keep AI models warm on their GPUs for immediate processing and can dynamically load models as tasks arrive, optimizing both response time and resource utilization.
This architecture is designed for scalability, enabling easy integration of additional Orchestrator and Gateway nodes as demand increases. Under the hood, it relies on a specialized ai-runner Docker image to execute inference requests on AI models, simplifying deployment and enhancing the scalability of new pipelines. Ongoing developments aim to enhance performance and broaden the container’s capabilities to support increasingly complex AI models and custom user-defined pipelines. Below is a simplified diagram illustrating the complete AI inference pipeline within Livepeer AI. Only one AI Orchestrator with one GPU on the same machine is shown for clarity. In reality, the AI Gateway is connected to multiple Orchestrators, each of which can have multiple worker nodes with various GPUs attached. This flow starts at the AI Gateway nodes, which route tasks from applications or users to an appropriate AI Orchestrator node. The selection of the AI Orchestrator node is based on factors such as the speed of previous inference requests, orchestrator stake, advertised price, and more. The AI Orchestrator node then executes the task in the ai-runner Docker Container. In this container, the AI Orchestrator can either:
  • Pre-load a model: Orchestrators keep frequently used models warm on GPUs, speeding up task processing.
  • Dynamically load a model: Orchestrators can load models on demand, allowing flexibility to handle various tasks.
During the selection process, to ensure quick response times, Gateways first check if there are any Orchestrators available with the requested model ‘warm’ on their GPU. If not, they route the task to an Orchestrator that can dynamically load the model. Once the model is loaded, the task is executed on the GPU, and the results are returned to the Orchestrator node, which then sends them back to the Gateway node and finally to the application or user that requested the task. This architecture ensures Livepeer AI is both efficient and flexible, ready to scale and adapt to various AI applications.

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Explore AI Pipelines

Livepeer AI hosts a variety of generative AI pipelines, each supporting different models. Current offerings primarily utilize Diffusion models. Plans are in place to expand support to include other model types in future updates.

AI Pipelines

Explore the AI Pipelines page to learn more about each pipeline and the models they support.

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Current Limitations and Future Directions

  • Beta Phase: Livepeer AI is currently in its Beta phase, and users may encounter bugs or issues during this early stage. It is not yet intended for high-demand production workloads.
  • Limited Set of Open-source Models Supported: Livepeer AI currently supports a limited set of open-source AI models and pipelines available on Hugging Face. However, this range is gradually expanding with the goal of supporting custom models in the future.
  • High VRAM GPUs Required for Most Pipelines: Currently, Livepeer AI requires GPUs with at least 16GB of VRAM to run most AI inference tasks effectively. For optimal performance and a higher chance of being selected for jobs, 30/40 series GPUs or comparable models are recommended. The exact GPU requirements can be found in the documentation for each pipeline on the AI Pipelines page.

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Livepeer AI Terminology

  • Mainnet Transcoding Network: Comprises Orchestrator and Gateway nodes that perform or coordinate transcoding tasks. The Orchestrator handles the supply-side operations, while the Gateway handles the demand-side.
  • Livepeer AI Network: A specialized subnet within the Livepeer network designed for managing AI inference tasks, connecting Providers, Gateways, Orchestrators, and Workers in a decentralized manner.
  • Mainnet Transcoding Network Orchestrator: A node responsible for handling transcoding jobs within the Mainnet Transcoding Network. The Orchestrator is a supply-side node that coordinates and ensures the completion of transcoding tasks. This term is distinct from the Worker, which executes the actual compute jobs.
  • Mainnet Transcoding Network Gateway: A node that acts as the clearinghouse or middleware, responsible for routing transcoding tasks to the appropriate Orchestrator nodes within the Mainnet Transcoding Network. Often referred to as the Gateway (formerly Broadcaster), it remains stateless, ensuring it only holds essential information for task routing and job verification.
  • Provider: An entity that operates one or more Gateways, potentially with additional services or developer experience (DevX) abstractions layered on top. Providers facilitate demand-side tasks in both the Mainnet Transcoding Network and the Livepeer AI Network.
  • Worker: Formerly referred to as the Transcoder, the Worker is a node that executes compute jobs. This can be running on the same machine as the Orchestrator or remotely. In the Livepeer AI Network, Workers execute AI inference tasks, while in the Mainnet Transcoding Network, they handle transcoding jobs.
  • AI Orchestrator: A specialized node within the Livepeer AI Network, responsible for executing AI inference tasks. The AI Orchestrator runs the required software and handles compute jobs for AI tasks, coordinating with Workers when needed.
  • AI Gateway: A demand-side node within the Livepeer AI Network responsible for directing AI-related tasks to the appropriate AI Orchestrator nodes. Similar to the Mainnet Gateway, it does not manage developer-related tasks (e.g., DevX persistence) and primarily handles job instruction and verification for AI inference.