ZENQIRA ZENQ Price
Introduction
ZENQIRA (ZENQ) offers Scalable, decentralized computing for AI training and complex computational tasks. More
Token Stats
| Fully Diluted Valuation | $882,992 |
|---|---|
| 24H Trading Volume | $6,050 |
| 24H Low / High | $0.00 / $ 0.00 |
| Circulating Supply | 0.00 |
| Total Supply | 500.00M |
| Max Supply | 500.00M |
Info
| Categories | DePIN 1 more |
|---|---|
| Founder | Sam Van Doorn |
| Website | zenqira.com Whitepaper 1 more |
| Socials | |
| Chains | Binance-smart-chain Ecosystem |
| Explorer | Binplorer 2 more |
| Contracts |
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About ZENQIRA $zenq
ZENQIRA (ZENQ) stands at the forefront of integrating decentralized computing power with the burgeoning field of artificial intelligence (AI), offering a platform that democratizes access to computational resources for AI training and beyond. Here's an in-depth exploration:
Project Concept:
Decentralized Computing for AI: ZENQIRA leverages the Golem Network's decentralized computing resources to provide scalable computational power for AI development. This approach addresses the high demand for computing power in AI training by distributing the workload across a network of users' computers, reducing costs and enhancing accessibility.
AI Infrastructure Support: By facilitating easier access to computational resources, ZENQIRA aims to support AI developers in training more sophisticated models without the prohibitive expense of maintaining large data centers or purchasing expensive hardware like GPUs.
Core Features:
Integration with Golem Network: ZENQIRA taps into the Golem Network, a decentralized marketplace for computing power. This integration allows users to utilize this vast computational bandwidth for their AI training needs.
Scalable AI Training Solutions: The platform offers tools and environments that can scale according to the computational demands of AI projects, from small-scale experiments to large, data-intensive training sessions.
Deflationary Token Model: Although specifics on tokenomics are not discussed, ZENQIRA's token model incentivizes participation by rewarding users for contributing their computing power, creating a self-sustaining ecosystem.
Community-Driven Approach: ZENQIRA fosters a community where both providers of computing power and AI developers benefit, promoting a collaborative environment for technological advancement.
Use Cases:
AI Research and Development: Researchers and developers can use ZENQIRA's services to run AI simulations, machine learning models, and deep learning projects without the need for extensive personal computing resources.
Enterprise AI Solutions: Businesses can leverage this platform for big data analysis, predictive modeling, or customer behavior analysis, using AI to gain insights without investing heavily in proprietary infrastructure.
Decentralized Computing Applications: Beyond AI, the platform supports any computing-intensive applications that can be distributed across a network, like rendering, scientific simulations, or blockchain processes.
Technological Innovations:
Harnessing Decentralized Networks: By using a network of individual nodes, ZENQIRA reduces the dependency on centralized data centers, promoting resilience and reducing single points of failure.
Advanced Blockchain Integration: ZENQIRA employs blockchain technology for transparent, secure transactions of computational power, ensuring that contributions and usage are fairly compensated.
Vision for the Future:
Expanding AI Accessibility: The goal is to make high-level AI training accessible to a broader audience, potentially including academics, startups, and independent developers.
Continued Technological Evolution: As AI and computing technology evolve, ZENQIRA aims to adapt its platform to new methodologies, perhaps integrating quantum computing when viable.
What is unique about ZENQIRA (ZENQ)?
ZENQIRA uniquely provides decentralized computing power for AI, making advanced AI training more accessible.
Who are the founders of ZENQIRA (ZENQ)?
ZENQIRA was founded by Sam Van Doorn, who brings a background in computer science with a focus on blockchain's potential in AI.