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The rapid advancement of generative AI has ushered in a new era of creativity and innovation. Powerful tools, like OpenAI's Sora and Stable Diffusion, have demonstrated the magic of text-to-video generation, attracting millions of users within mere months of publicly launching their latest AI products. Yet there still remains one major hurdle for AI to reach its full potential: the costly centralization of AI compute resources, which has led to major bottlenecks that drive up prices for developers and creators alike.
Decentralized AI services offer a compelling alternative. By distributing computational resources across a network of individual contributors, a decentralized approach can dramatically reduce costs, increase accessibility, and foster a more open and innovative ecosystem. Here, we explore how developers can create a completely decentralized generative AI tech stack, and the benefits of doing so.
The Decentralized Gen AI Tech Stack
Infrastructure and Compute Resources
Livepeer AI
Akash Network
Foundation Models
EleutherAI’s GPT-J
BLOOM (BigScience)
Development Frameworks
PySyft
FATE
Fine-Tuning and Adaptation Tools
Hugging Face’s Adapters
PET (Pattern-Exploiting Training
Data Processing and Management
Livepeer AI
Filecoin
Generative AI is dependent on the computational infrastructure it runs on. In a decentralized ecosystem, this infrastructure is distributed across numerous nodes, offering advantages in scalability, cost-effectiveness, and resilience.
Livepeer AI is a decentralized network optimized for AI video processing. It leverages its widely distributed, established hardware providers, who are already transcoding millions of minutes of traditional video every week with plenty of GPU capacity available, to also support affordable generative AI tasks.
Benefits:
AI video focus optimizes text-to-image, image-to-image, and image-to-video conversions
Pay-per-task model more cost-effective than reserving expensive centralized capacity
Eliminates single-point-of-failure risk inherent to highly centralized cloud servers
Akash is a decentralized cloud computing marketplace focused on container deployments, with unique capabilities in hosting machine learning models for inference and running validators and nodes for various blockchains, often at prices significantly lower than centralized providers.
Benefits:
Container-native structure is optimized for deploying containerized applications
High performance by leveraging unutilized data center capacity
Interoperability allows it to support various container orchestration tools
Livepeer AI is a tailored choice if your primary focus is video processing, streaming, or media-related applications, as it is specifically optimized for video transcoding, AI-driven video enhancements, and streaming scalability.
Akash Network may be better suited for broader compute use cases, including deploying AI/ML models, decentralized applications, or blockchain nodes.
Foundation models, also known as large AI models, are advanced AI systems trained on large amounts of data to perform a wide range of tasks, such as generating output from prompts. They serve as the bedrock upon which generative AI applications are built. While centralized models like GPT-3 or DALL-E have garnered significant attention, their decentralized counterparts offer unique advantages.
The open-source language models developed by EleutherAI, including GPT-J and newer GPT-NeoX, are designed to rival the capabilities of OpenAI's GPT-3 — so much so that Jack Clark, the author of the Import AI newsletter, has called it an “attack on the political economy of AI.”
Benefits:
Open-source nature allows for transparency and community-driven improvements
Can be run locally, reducing dependency on cloud services
No usage restrictions or API costs associated with proprietary models
BLOOM is a multilingual large language model, created through a collaborative open source effort involving more than 1000 researchers worldwide.
Benefits:
Supports 46+ languages, and can be fine-tuned for specific domains or languages
Enables research and innovation without commercial constraints
Community-driven development ensures diverse perspectives
EleutherAI may be your choice if you prioritize greater open-source flexibility, stability, and a more grassroots researcher community.
BLOOM is a better option If multilingual support, a globally diverse development approach, and a deliberative focus on ethical AI are important to you.
Development frameworks play a crucial role in the creation and deployment of AI models, supporting distributed computing, ensuring data privacy, and facilitating collaborative development. They play a role throughout the entire development process, from data preparation and training to testing and deployment.
PySyft is an open-source library for secure and private Deep Learning, developed by OpenMined. It preserves privacy by enabling computation on encrypted data without decrypting it first.
Benefits:
Multi-party computation allows collaborative AI development without sharing raw data
Interoperabile with popular deep learning frameworks like PyTorch and TensorFlow
Extensible, supporting custom privacy-preserving operations and protocols
FATE is an open-source project initiated by Webank's AI Department to provide a secure computing framework for federated AI, with a modular design for flexible deploymenet and customization.
Benefits:
Comprehensive support for various federated learning algorithms and architectures
Scalable design optimized for large-scale industrial applications
Works cross-platform with support throughout heterogeneous computing environments
PySyft’s decentralized model can empower many privacy-preserving use cases in particular, such as collaborative health care research on sensitive patient data or fraud detection across multiple banks without sharing customer data.
FATE’s cross-organizational features can help aid collaborative model training across different companies or institutions, as well as offer federated learning for distributed sensor networks in fields like IoT or Edge Computing.
While foundation models provide powerful general-purpose capabilities, fine-tuning and adaptation tools allow developers to tailor these models for specific tasks or domains. In a decentralized AI ecosystem, these tools play a crucial role in democratizing AI development and enabling customization without the need for extensive computational resources.
Hugging Face Adapters are lightweight, trainable modules that can be added to pre-trained models to adapt them for specific tasks without fine-tuning the entire model, using significantly less computational resources in the process.
Benefits:
Multiple adapters can be trained for different tasks and swapped out as needed
Can be trained on sensitive data without exposing the entire model
Easier to manage and version control compared to full model checkpoints
PET is a few-shot learning technique that leverages task-specific patterns to improve model performance with limited labeled data (a scenario in which there is a scarcity of annotated information available for training machine learning models).
Benefits:
Data efficient, achieving good performance with very few labeled examples
Enables AI applications in domains where large labeled datasets are scarce or expensive to obtain
Facilitates distributed learning scenarios where data cannot be centralized
Hugging Face Adapters are ideal if you need parameter-efficient fine-tuning, modularity, and want to benefit from the ecosystem provided by Hugging Face.
Pattern-Exploiting Training (PET) may be preferable if you need label-efficient learning, are operating in a low-data environment, and prefer a prompt-based learning approach for tasks like classification or few-shot learning.
Efficient and secure data management is crucial for AI development, especially in a decentralized ecosystem. This component of the stack focuses on how data is stored, shared, and processed in a distributed manner.
Filecoin is a decentralized storage network that turns cloud storage into an algorithmic market, enabling distributed long-term storage of large AI data sets and archiving of AI research data, among other use cases.
Benefits:
Distributed storage leverages unused storage capacity around the world
More resilient data, distributed across multiple nodes for redundancy
Verifiable through cryptographic proofs showing that data is being stored correctly
While primarily an infrastructure provider, Livepeer AI also plays a crucial role in data processing, particularly for video-related generative AI tasks. What’s more, it is the first open infrastructure network to join the Coalition for Content Provenance and Authenticity (CP2a), an open technical standard working to provide publishers, creators, and consumers the ability to trace the origin of different types of media.
Benefits:
Supports video processing tasks: upscaling, frame interpolation, and subtitle generation
Greater resiliency and censorship resistance reduces risk of data loss or unavailability
Working to provide verification for the origin and authenticity of AI video content
Filecoin is best used when your focus is on general decentralized data storage, particularly for large-scale data that needs long-term security and redundancy.
Livepeer AI is ideal when you need to process, manage or enhance video content in real-time, especially when AI/ML video analysis or cost-efficient streaming is important for your platform.
You can get started testing your decentralized AI tech stack with Livepeer AI, which has been optimized by our community to streamline generative AI tasks for developers and creators.
The rapid advancement of generative AI has ushered in a new era of creativity and innovation. Powerful tools, like OpenAI's Sora and Stable Diffusion, have demonstrated the magic of text-to-video generation, attracting millions of users within mere months of publicly launching their latest AI products. Yet there still remains one major hurdle for AI to reach its full potential: the costly centralization of AI compute resources, which has led to major bottlenecks that drive up prices for developers and creators alike.
Decentralized AI services offer a compelling alternative. By distributing computational resources across a network of individual contributors, a decentralized approach can dramatically reduce costs, increase accessibility, and foster a more open and innovative ecosystem. Here, we explore how developers can create a completely decentralized generative AI tech stack, and the benefits of doing so.
The Decentralized Gen AI Tech Stack
Infrastructure and Compute Resources
Livepeer AI
Akash Network
Foundation Models
EleutherAI’s GPT-J
BLOOM (BigScience)
Development Frameworks
PySyft
FATE
Fine-Tuning and Adaptation Tools
Hugging Face’s Adapters
PET (Pattern-Exploiting Training
Data Processing and Management
Livepeer AI
Filecoin
Generative AI is dependent on the computational infrastructure it runs on. In a decentralized ecosystem, this infrastructure is distributed across numerous nodes, offering advantages in scalability, cost-effectiveness, and resilience.
Livepeer AI is a decentralized network optimized for AI video processing. It leverages its widely distributed, established hardware providers, who are already transcoding millions of minutes of traditional video every week with plenty of GPU capacity available, to also support affordable generative AI tasks.
Benefits:
AI video focus optimizes text-to-image, image-to-image, and image-to-video conversions
Pay-per-task model more cost-effective than reserving expensive centralized capacity
Eliminates single-point-of-failure risk inherent to highly centralized cloud servers
Akash is a decentralized cloud computing marketplace focused on container deployments, with unique capabilities in hosting machine learning models for inference and running validators and nodes for various blockchains, often at prices significantly lower than centralized providers.
Benefits:
Container-native structure is optimized for deploying containerized applications
High performance by leveraging unutilized data center capacity
Interoperability allows it to support various container orchestration tools
Livepeer AI is a tailored choice if your primary focus is video processing, streaming, or media-related applications, as it is specifically optimized for video transcoding, AI-driven video enhancements, and streaming scalability.
Akash Network may be better suited for broader compute use cases, including deploying AI/ML models, decentralized applications, or blockchain nodes.
Foundation models, also known as large AI models, are advanced AI systems trained on large amounts of data to perform a wide range of tasks, such as generating output from prompts. They serve as the bedrock upon which generative AI applications are built. While centralized models like GPT-3 or DALL-E have garnered significant attention, their decentralized counterparts offer unique advantages.
The open-source language models developed by EleutherAI, including GPT-J and newer GPT-NeoX, are designed to rival the capabilities of OpenAI's GPT-3 — so much so that Jack Clark, the author of the Import AI newsletter, has called it an “attack on the political economy of AI.”
Benefits:
Open-source nature allows for transparency and community-driven improvements
Can be run locally, reducing dependency on cloud services
No usage restrictions or API costs associated with proprietary models
BLOOM is a multilingual large language model, created through a collaborative open source effort involving more than 1000 researchers worldwide.
Benefits:
Supports 46+ languages, and can be fine-tuned for specific domains or languages
Enables research and innovation without commercial constraints
Community-driven development ensures diverse perspectives
EleutherAI may be your choice if you prioritize greater open-source flexibility, stability, and a more grassroots researcher community.
BLOOM is a better option If multilingual support, a globally diverse development approach, and a deliberative focus on ethical AI are important to you.
Development frameworks play a crucial role in the creation and deployment of AI models, supporting distributed computing, ensuring data privacy, and facilitating collaborative development. They play a role throughout the entire development process, from data preparation and training to testing and deployment.
PySyft is an open-source library for secure and private Deep Learning, developed by OpenMined. It preserves privacy by enabling computation on encrypted data without decrypting it first.
Benefits:
Multi-party computation allows collaborative AI development without sharing raw data
Interoperabile with popular deep learning frameworks like PyTorch and TensorFlow
Extensible, supporting custom privacy-preserving operations and protocols
FATE is an open-source project initiated by Webank's AI Department to provide a secure computing framework for federated AI, with a modular design for flexible deploymenet and customization.
Benefits:
Comprehensive support for various federated learning algorithms and architectures
Scalable design optimized for large-scale industrial applications
Works cross-platform with support throughout heterogeneous computing environments
PySyft’s decentralized model can empower many privacy-preserving use cases in particular, such as collaborative health care research on sensitive patient data or fraud detection across multiple banks without sharing customer data.
FATE’s cross-organizational features can help aid collaborative model training across different companies or institutions, as well as offer federated learning for distributed sensor networks in fields like IoT or Edge Computing.
While foundation models provide powerful general-purpose capabilities, fine-tuning and adaptation tools allow developers to tailor these models for specific tasks or domains. In a decentralized AI ecosystem, these tools play a crucial role in democratizing AI development and enabling customization without the need for extensive computational resources.
Hugging Face Adapters are lightweight, trainable modules that can be added to pre-trained models to adapt them for specific tasks without fine-tuning the entire model, using significantly less computational resources in the process.
Benefits:
Multiple adapters can be trained for different tasks and swapped out as needed
Can be trained on sensitive data without exposing the entire model
Easier to manage and version control compared to full model checkpoints
PET is a few-shot learning technique that leverages task-specific patterns to improve model performance with limited labeled data (a scenario in which there is a scarcity of annotated information available for training machine learning models).
Benefits:
Data efficient, achieving good performance with very few labeled examples
Enables AI applications in domains where large labeled datasets are scarce or expensive to obtain
Facilitates distributed learning scenarios where data cannot be centralized
Hugging Face Adapters are ideal if you need parameter-efficient fine-tuning, modularity, and want to benefit from the ecosystem provided by Hugging Face.
Pattern-Exploiting Training (PET) may be preferable if you need label-efficient learning, are operating in a low-data environment, and prefer a prompt-based learning approach for tasks like classification or few-shot learning.
Efficient and secure data management is crucial for AI development, especially in a decentralized ecosystem. This component of the stack focuses on how data is stored, shared, and processed in a distributed manner.
Filecoin is a decentralized storage network that turns cloud storage into an algorithmic market, enabling distributed long-term storage of large AI data sets and archiving of AI research data, among other use cases.
Benefits:
Distributed storage leverages unused storage capacity around the world
More resilient data, distributed across multiple nodes for redundancy
Verifiable through cryptographic proofs showing that data is being stored correctly
While primarily an infrastructure provider, Livepeer AI also plays a crucial role in data processing, particularly for video-related generative AI tasks. What’s more, it is the first open infrastructure network to join the Coalition for Content Provenance and Authenticity (CP2a), an open technical standard working to provide publishers, creators, and consumers the ability to trace the origin of different types of media.
Benefits:
Supports video processing tasks: upscaling, frame interpolation, and subtitle generation
Greater resiliency and censorship resistance reduces risk of data loss or unavailability
Working to provide verification for the origin and authenticity of AI video content
Filecoin is best used when your focus is on general decentralized data storage, particularly for large-scale data that needs long-term security and redundancy.
Livepeer AI is ideal when you need to process, manage or enhance video content in real-time, especially when AI/ML video analysis or cost-efficient streaming is important for your platform.
You can get started testing your decentralized AI tech stack with Livepeer AI, which has been optimized by our community to streamline generative AI tasks for developers and creators.
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