The AI Inference Industry Is Ripe For Decentralization

Artificial intelligence workloads are composed of training and inference processes, wherein they first learn and acquire knowledge and then use it in real-world applications.

AI training is the process through which we develop and fine-tune models, and involves feeding massive amounts of data into them. The AI models become more intelligent by analyzing patterns within these datasets, extracting insights and learning from them. Meanwhile, AI inference involves using those trained AI models in various applications, such as text, image and video generation. 

Some of the most advanced AI models today are built using neural networks inspired by the human brain, but they’re not nearly as efficient as the neurons found inside the minds of humans. Because of this inefficiency, it can cost millions of dollars to train new models. Estimates of the cost of training OpenAI’s GPT-3 large language model range from around $500,000 to as much as $4.6 million, most of that being spent on computational resources such as graphics processing units.

Training AI is extremely expensive, but even these costs are negligible compared to the price of running AI inference. As IBM points out, the average AI model spends up to 90% of its lifecycle in inference mode. In the case of GPT-3, for instance, which powers the ChatGPT chatbot, it must tap into expensive computational resources every time it’s queried or prompted by its millions of users worldwide. 

AI Inference Infrastructure

Today, the vast majority of AI inference workloads are run in the cloud, using resources provided by cloud computing giants such as Amazon Web Services, Microsoft Azure, Google Cloud and Oracle. 

These companies have come to dominate the AI inference industry because of the huge investment required. They spend millions of dollars purchasing the infrastructure, such as the GPUs, servers and storage resources and paying the steep electricity bills associated with running that hardware. The average company cannot afford to take on such a significant financial burden, which means that they have few options besides paying for cloud services.

However, there is an opportunity for decentralized computing infrastructures to provide an alternative to these centralized cloud platforms. Decentralized AI refers to the idea of distributing AI inference tasks across a vast network of nodes, which can be high-powered servers and graphics cards, or much less powerful and expensive devices, such as laptops and even smartphones. Because AI inference workloads are performed by multiple devices working in concert with one another, it provides an alternative to cloud-hosted servers, and it can also work out much cheaper, democratizing access to this exciting new technology. 

Why Should We Decentralize AI Inference?

These decentralized AI inference networks are still in their infancy, but the idea is gaining traction. NeurochainAI is building an innovative AI inference platform that operates on an “infrastructure-as-a-service” model. It is pioneering an ecosystem based on cooperation and coordination, where every stakeholder is rewarded for their participation. 

NeurochainAI’s network is built atop the blockchain and uses smart contracts to automate the payment of these rewards. In addition, these are determined based on the amount of computing resources each participant devotes to it. Such a model incentivizes people to contribute to the network, paving the way for a decentralized AI economy that everyone can join. 

This decentralized network is made up of hundreds of thousands of devices, and it’s open for anyone to participate. It takes advantage of the fact that there are billions of smartphones and tablets in the world. Always connected to the internet but often not being used. What that means is there’s an enormous pool of underutilized computational capacity in the world.

AI inference tasks are distributed across the thousands of nodes that make up NeurochainAI’s network, and it has the effect of creating a more robust infrastructure platform for AI. Unlike cloud infrastructures, which have a single point of failure. NeurochainAI will remain up and running even if hundreds of smartphones suddenly go offline. Moreover, such a network is significantly cheaper, as the upfront costs of the hardware have already been paid by device owners. 

The potential of distributed AI inference goes beyond resiliency and lower costs, as it will also provide an avenue to ease the strain on existing server networks. One of the challenges faced by centralized cloud computing providers is that they’re bound by their finite capacity and resources. As such, there is often a wait for access to AI inference resources. However, a globally distributed network such as NeurochainAI’s can utilize parallel processing to disburse inference workloads across thousands of nodes, ensuring adequate resources are always available. 

Sharing The Benefits Of AI

Decentralizing AI inference ensures that the benefits of AI and the revenues it derives will not be monopolized by a select few entities, but will instead be available to all. This is important because AI is progressively becoming more intelligent, playing a more important role in everybody’s lives. With decentralized inference, those who use AI will also be able to participate in the AI economy.

These are good reasons to believe that the future of AI inference will be decentralized, based on values such as community participation and equitable access for all. ess and benefit from them.