Ethereum changed from a PoW to a PoS mechanism, leaving hundreds of thousands of people with ETH-focused mining rigs stranded. There were claims that many former Ethereum miners began exploring ways of using the mining chips in a new and trending venture, AI. 

The speed at which AI took over last year is stuff for the legends— and it’s this speed that attracted Ethereum miners. 

However, this birthed a new dispute; can crypto mining chips be efficiently used in running AI programs? The answer is yes; as computing components, mining chips can offer some use for AI. But are they perfect for such tasks? Here are five reasons why crypto mining chips are not suitable for AI.

ASIC miners design ‘flaw’

Of course, everyone in the mining space is aware of the actualization of the idea of application-specific integrated circuits. By design, ASICs are fashioned to only work on a specific task, in this case, mining.

The designers even define the exact parameters under which these chips can function. For instance, some ASICs can only be used in mining SHA 256 crypto assets.

Take, for instance, a miner designed for Bitcoin. Bitcoin currently leverages the SHA256 algorithm. Hence, only miners designed to deal with this algorithm can mine Bitcoin.

So, what does this mean for AI? The mere actuality that ASICs have self-defined purposes makes them useless for AI.

Miners not optimized for machine learning workloads

Yes, the design flaw of miners and their cards means these devices can mainly work on tasks such as mining. However, another simpler way to look at this design flaw is by saying that the rigs are never optimized for machine learning workloads.

Many tasks and machine learning workloads require different setups. These AI modules include image recognition, image processing, translation, document processing and analysis, and many more. Many of these tasks cannot be completed by the setup of mining rigs in their basic form. This brings us to our 3rd point, differing needs. 

A Tale of Conflicting Needs

Mining and Artificial intelligence systems are very distinct in operation and have differing needs. Mining as a system only requires mere operations associated with computations. In mining, the systems require cards projecting massive amounts of hash power. This is because all factors are kept constant; miners require high hashes to complete any block.

Miners don’t need high vRAMs, i.e., RAMs designed for videos. Graphic manipulation is not something miners do. Their primary role is solving the necessary cryptographic equations.

On the contrary, AI is designed with a high need for vRAMs. A large part of AI technology relies on such cards as vRAMs. vRAMs can store data but also handle the operation of data simultaneously.

When speaking about AI, some note that the GPUs are not entirely useless but are much less inefficient when given practical tasks of training AI. AI training needs data in monumental proportions, and GPU can’t provide such. AI training relies more on vRAMs, which are equivalent to crowdsourcing. So, GPUs designed in mining assets like BTC could be useless in an AI application. 

The massive electricity consumption

Another issue exposing the flaw of mining devices and chips is their thirst for electricity. Normal computers are nothing compared to mining rigs which consume huge amounts of power only to mine small, almost insignificant amounts of coins. 

An average computer designed to train AI is just a small 150CC motorbike compared to a 1k CC bike, i.e., mining rigs. Why is using the miners, which are highly powered, not a good choice when training AI? Well, the simple answer is efficiency. 

AI training and machine learning are continuous processes. It involves garnering data in large sums from thousands, possibly millions of sources continually. It could take months, possibly years, to fully prepare an AI. Using mining rigs that consume high amounts of power doesn’t sound very economical when completing such a task. As such, mining rigs should not be used for AI training on that account. 

Issues like Memories and Storage Capacity

Mining is much less focused on factors like memories and storage capacity. While storage capacity could be key, it’s not as vital as in the case of AI and machine learning.

In mining, a desktop with 8GB of RAM and 256GB of hard drive space is enough to run the software and start earning. This is extremely low, especially when dealing with a large machine learning project and AI training.

AI requires massive amounts of working memory and data storage to function effectively. The data is collected from millions of sources and needs proper storage mechanisms like a cloud, hard drive or even SSD.

Final word

So, this guide has been looking keenly at the five reasons why tools used in mining crypto could fall short when used in AI-related projects. Many ultra-modern miners are designed to handle just a single type of computation. By design, they lack the operational abilities to deal with machine learning workloads. 

Other reasons include conflicting needs, high electricity consumption and the puzzle of memories and storage. All these combined give reasons why it’s not efficient to use miners in machine learning and AI.

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