Adds Support for Apple’s Silicon Chips for AI, a DePIN project, has integrated Apple’s silicon chip hardware for its machine learning (ML) and artificial intelligence (AI) products. The company shared the news, noting that it was part of its expansion plans. 

The move means that ML engineers can leverage to easily roll out thousands of Apple chips in a single cluster. There’s been an AI rave in recent months. And seeks to expand the use of AI by making it easier for more people to access GPU compute options for AI and ML use cases. Enhances AI with Support for Apple Silicon Chips

Meanwhile, Apple chips integrate CPU, GPU, and an improved Neural Engine onto a single chip. This leads to increased data processing efficiency. And lower latency which AI and ML apps need. will support top-quality Apple silicon chips, such as:

  • M1, 
  • M1 Max, 
  • the M1 Pro, 
  • M1 Ultra; M2,
  • M2 Max, 
  • M2 Pro, 
  • M2 Ultra. 
  • M3, M3 Max, and M3 Pro.

Apple’s M3’s memory structure supports up to 128GB. This means it outperforms graphics cards with NVIDIA A100-80GB capacity. In addition, an enhanced neural engine powers the M3 chips. This operates 60% faster than its M1 series.

Apple’s M3 chips boast a unified memory structure. This makes them suitable for model inference. This involves running real-time data through an AI model to solve problems or provide predictions. 

How significant is this?

Reacting to the move, Ahmad Shadid, founder said, “This is a massive step forward in democratizing access to powerful computing resources, and paves the way for millions of Apple users to earn rewards for contributing to the AI revolution.” allows users to access affordable GPU computing resources for AI and ML applications. It uses Solana to provide rewards for users who provide GPU and CPU capacity to the IO Network. 

 Apple product owners can now easily onboard their devices and earn rewards. In addition, supporting Apple’s silicon chips will provide the IO network with more access and capacity globally. This would enable the network to continue developing the Internet of GPUs to tackle the compute scarcity caused by the ongoing growth in AI and ML. 

About sets up and manages decentralized GPU clusters in the IO Network. The IO Network allows users to access hundreds of thousands of GPUs globally. These GPUs are to meet the growing needs of AI and ML.  

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