Aethir Launches Aethir Claw Alpha for AI Agent Hosting with $400M in Distributed Compute

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Aethir has launched the alpha phase of Aethir Claw, an AI agent hosting platform built on $400 million in distributed compute. The platform uses isolated VPS environments and zero provider access to safeguard data sovereignty for API keys and logs. It plans to introduce native crypto features such as token tracking and whale monitoring, supporting ATH and USDT. Aethir’s GPU network spans 94 countries, offering 435,000 GPU containers at up to 80% lower costs than traditional cloud providers. Recent AI and crypto developments highlight the growing trend of integrating on-chain tools with machine learning, while inflation data underscores rising demand for scalable decentralized solutions.

Aethir Claw fundamentally disrupts the traditional托管 service model of “renting third-party cloud resources and reselling them” by directly tapping into Aethir’s own distributed computing resources, valued at over $400 million. Through Aethir Claw, users gain access to fully isolated VPS environments and can opt for a “zero provider access” mode, ensuring absolute root-level data sovereignty over AI agent API keys, conversation logs, and executed actions. The platform comes pre-installed with OpenClaw and will undergo deep customization for specific business scenarios in later stages, with plans to integrate native crypto capabilities such as token tracking, on-chain alerts, and whale monitoring. It supports payments in cryptocurrencies including ATH and USDT, as well as fiat currencies. In subsequent phases, Aethir will introduce a “Model-as-a-Service” (MaaS) layer, incorporating open-source large model inference and text-to-image/video generation powered by Aethir’s GPUs, fully entering the agentic AI application market. As the world’s first decentralized GPU infrastructure to offer B300 accelerators, Aethir provides enterprise clients with automated Kubernetes GPU cluster management at scale, spanning 94 countries and regions. Over 435,000 GPU containers across more than 200 nodes deliver ultra-low-latency compute access, reducing overall computing costs by 60% to 80%.

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