Network Operation of Syneris

Hybrid GPU and CPU Architecture

Syneris utilizes both GPU and CPU resources, allowing for a versatile network capable of handling a wide array of computational tasks. The network draws computational power from individuals and organizations willing to contribute their unused resources. This model not only enhances the network's capacity but also incentivizes participation through rewards.

The hybrid approach allows for quick scaling of resources as demand increases, making it adaptable to various workloads, including AI training and processing. By utilizing contributed resources, Syneris minimizes operational costs, making advanced computing more accessible to developers and researchers.

Strategic Integration with De-GPU Networks

Syneris integrates with the existing model, tapping into its powerful GPU resources to enhance its own capabilities. These collaborations further expands Syneris's computational reach, ensuring that product development can proceed smoothly.

By leveraging existing powerful networks, Syneris can quickly execute product development while building its infrastructure, allowing for parallel progress.This strategic integration helps balance resource utilization, ensuring that Syneris can meet immediate demands without compromising on performance.

Establishing Syneris's Network Infrastructure

As Syneris's own network grows, the reliance on third-party resources will diminish. This transition is crucial for establishing a self-sustaining ecosystem.Over time, Syneris aims to utilize its dedicated computational resources for running AI models, leading to increased efficiency.

By developing and relying on its infrastructure, Syneris significantly lowers operational costs associated with third-party services.Managing its resources allows Syneris to optimize performance and tailor solutions to meet specific user needs, ensuring a higher quality of service.

This operational strategy ensures that Syneris remains scalable and flexible, adapting to the evolving landscape of AI and decentralized computing. By balancing immediate needs with long-term goals, Syneris is well-positioned to thrive in the competitive landscape of AI technology.

Case study: High Cost GPUs is barrier for small companies

Hardware costs for AI primarily involve GPUs and CPUs, with high-end GPUs like Nvidia’s A100 costing around $10,000 or $1.14/hour on Google Cloud. OpenAI’s GPT-4 training cost over $100 million on Azure.

Maintaining AI systems also incurs ongoing costs, needing resources like the 1,920 CPUs and 280 GPUs for DeepMind’s Alphago, plus updates and downtime from hardware failures. AI solutions range from thousands for chatbots to millions for advanced models. Companies like Netflix spend heavily on AI, as do Google, Facebook, and Amazon, with AI budgets in the billions.

For example, Netflix spends 1,5 billons on technology annually. A chunk of their tech budget is spent on artificial intelligence. AI helps them personalize recommendations for each individual user and also automate many of their processes, like creating subtitles.

Decentralized Protocols as Syneris will be promising solution by building DeGPU Network. Besides, no-code platforms to build AIs can assist with all sorts of application use cases at very affordable prices. Affordable AI options include no-code platforms like Syneris, offering pay-as-you-go machine learning without high monthly fees.