Share Dialog

Inferium is a multifaceted project that encompasses both AI model deployment and real estate investment platforms. This article focuses on the AI aspect, specifically the Inferium AI platform, which streamlines AI model deployment for developers. Additionally, it covers the funding and summarizes tasks for participating in its airdrop event.
Inferium AI serves as an intelligent store and aggregator for AI inference, empowering developers to deploy and list their models efficiently. It provides users with streamlined access to tailored models through machine learning (ML) capabilities. The platform has allocated 24% of its total token supply of 250 million tokens to community development initiatives
Funding
While specific details about funding rounds are limited in publicly available information, Inferium has attracted investments from various sources. Notable investors include Connectico Capital among others. The exact amounts raised are not fully disclosed across all rounds.
Airdrop Event Summary
To participate in the Inferium airdrop event, users must engage with the platform's incentivized MVP program by completing several tasks:
Registration: Sign up on the Inferium dashboard using your email address.
Profile Setup: Complete your profile setup.
Wallet Connection: Optionally connect your wallet and social accounts.
Platform Tasks: Complete all available tasks in the "Earn Inferno" section.
Daily Check-ins: Perform daily check-ins consistently.
Galxe Quests: Participate in Galxe quests by connecting required accounts
Maximizing Rewards:
To increase potential rewards:
Never miss daily check-ins.
Complete all available tasks promptly.
Engage with community discussions regularly.
By following these steps and maintaining consistent activity on the platform, users can position themselves for potential rewards during future token distributions. This summary highlights key aspects of Inferium's approach to community engagement through its airdrop event while emphasizing its role as an innovative solution for deploying AI models efficiently.
Shishio Makoto
No comments yet