How SynonAI?
Last updated
Last updated
SynonAI partners with a network of NVIDIA Cloud Partners, Infrastructure Funds, and other hardware partners such as io.net, Aethir and more to bring cost-effective and significant scale to AI enterprises.
Centralized AI training often relies on expensive, proprietary hardware resources. SynonAI breaks free from this model by leveraging the collective power of a decentralized network. By incorporating GPU (Graphics Processing Unit) capabilities into its validator nodes, SynonAI distributes the computational workload across the network, enabling efficient and cost-effective training of even the most complex AI models.
SynonAI streamlines the development process for experienced blockchain developers by utilizing a parallel-execution EVM (Ethereum Virtual Machine) stack. This familiar environment minimizes the learning curve for Solidity developers, allowing them to seamlessly transition to building on-chain AI applications.
A critical innovation within SynonAI is teeML (Trusted Execution Environment Machine Learning). teeML carves out a secure enclave within the validator nodes. This trusted execution environment allows for secure queries to be processed on both open-source and closed-source large language models (LLMs) without compromising their confidentiality. This ensures verifiable and tamper-proof results, fostering trust in the AI models operating on the SynonAI network.
To handle the demands of complex AI models and a potentially ever-growing user base, SynonAI prioritizes scalability. The network architecture is designed for high transaction throughput, ensuring tasks are processed swiftly, and minimal latency, minimizing delays in computation. This allows the network to scale efficiently as the demand for its services increases.
The backbone of SynonAI is a network of validator nodes, acting as the distributed training ground for your AI models. Each validator node contributes processing power through powerful GPUs, ideal for training demanding models like image generators, voice changers, and chatbots. Additionally, a secure enclave called teeML (Trusted Execution Environment Machine Learning) is established within each node. This trusted environment safeguards the confidentiality of your AI model during training, even for closed-source models.
As a user, you can choose from pre-built AI models for tasks like image generation, voice changing, or chatbot development, or upload your own custom model for training. Specify the desired training parameters like data amount, accuracy level, and training duration. Finally, indicate the number of GPUs you wish to rent for training. SynonAI allows you to scale your training job by allocating more or fewer GPUs distributed across the validator network.
The SynonAI platform acts as a marketplace, connecting you with available GPU resources on the validator nodes. Based on your requested resources and training parameters, the platform creates a secure training environment. Your AI model is then sliced into smaller pieces and distributed across the validator nodes. Each node leverages its GPU to train its assigned portion of the model in parallel. This distributed approach significantly reduces overall training time compared to traditional centralized methods.
Within the secure teeML enclave of each validator node, training data is processed securely, ensuring the confidentiality of your data and model, even for closed-source models. Once training on individual nodes is complete, the results are securely aggregated back to the SynonAI platform using cryptographic techniques to ensure data integrity and prevent tampering. The aggregated results are used to iteratively improve the overall AI model until the desired accuracy level is achieved.
Upon successful training, the final, improved AI model is available for you to download and use for its intended purpose. Optionally, you can choose to integrate your trained model back into the SynonAI marketplace, allowing other users to leverage your model for their own projects, creating a collaborative ecosystem of AI development.
SynonAI offers several benefits. By utilizing a network of distributed GPUs, it eliminates the need for expensive, proprietary hardware, making complex AI training accessible to a wider range of users. The teeML enclaves ensure the confidentiality of your data and model during training. Additionally, the decentralized nature of the network provides transparency and prevents any single entity from controlling the training process. Finally, SynonAI's architecture is designed for scalability, easily handling ever-growing user demands and complex AI models by adding more validator nodes to the network, ensuring efficient and fast training times.
SynonAI empowers a new era of AI development by enabling secure, efficient, and scalable training through its decentralized network. By leveraging the collective power of distributed GPUs and secure enclaves, SynonAI breaks down the barriers to entry for complex AI projects, fostering innovation and collaboration within the AI community.
