Cryptocurrency mining has revolutionized the way we look at online transactions. The ability to mine digital currencies has given investors a high level of autonomy and control over their assets. The encryption process is difficult to tamper with and the mining technology behind it has grown significantly. Just like any other technology, blockchain has a potential for multiple applications. Among them is Artificial Intelligence(AI).
Many people have attempted to use their crypto mining chips to perform AI computations. But the truth is, they’re not capable of producing the kind of computation power that’s needed. In this article, we’ll explore the reasons why crypto mining chips aren’t suitable for AI computations and what alternatives exist.
First, we need to understand what makes AI computations different from cryptocurrency mining. AI is an intricate set of algorithms that processes large amounts of data. It requires a considerable amount of processing power to train the algorithms on a vast dataset. The operations performed in AI processes are more complex than those done during cryptocurrency mining.
In cryptocurrency mining, chips are doing simple mathematical calculations that mainly involve hashing the transaction details and solving mathematical equations. Depending on the blockchain algorithm, computation times can range from milliseconds to seconds. AI computations, on the other hand, require chips capable of running highly complex algorithms. They need to perform matrix computations that require massively parallel processing. The computations are much more significant and take a lot more time.
Secondly, mining chips lack the necessary parallel processing properties that are essential in AI computations. In AI computations, data is processed in parallel across multiple cores on a chip. The more cores a chip has, the higher the computation efficiency due to decreased computation time. Crypto mining chips usually have a few cores due to the simplicity of the mining process. This results in a considerable bottleneck in AI computations since each core needs to complete its assigned computation before the next one starts. This leads to longer computation times and higher costs.
Another distinguishable difference is power consumption. The computational requirements for cryptocurrency mining are significantly different from AI computations, and therefore, the power consumption should also follow suit. Mining chips are designed to use a lot of power for short bursts, while AI chips demand sustained high power consumption. It’s not unusual for AI computation appliances to consume as much as 500W of power that’s sustained over long periods.
Furthermore, the physical size of the chips could be a limitation. Mining chips are usually relatively small and compact since they are designed to do computations that can fit within a small physical space. But the power consumption requirements for AI chips dictate that they be bulkier in size. This requirement for increased thermal management and cooling makes it difficult for a conventional cryptocurrency mining rig to accommodate and handle chips suitable for AI computations.
The last but equally important factor is latency. Cryptocurrency mining chips do not need to worry much about the response time or latency for the majority of the computations involved during cryptocurrency mining. However, in AI computations, the chips must have extremely low latency for tasks like neural network inference and object detection. The slightest delay in receiving a response can have a critical impact on the overall reliability of the algorithm.
So if mining chips aren’t suitable for AI computations, what alternatives exist?
The traditional CPU/GPU combination is a viable option, but it comes at a significant cost. The power requirements of computer systems designed for AI computations reach high levels, and upgrading an entire rig with high-performance CPUs, GPUs, and other components can be prohibitively expensive.
ASICs (application-specific integrated circuits) designed for artificial intelligence are a better alternative. These chips are custom-made for specific AI applications, allowing for optimized performance. They’re also more power-efficient and can handle larger amounts of data. Moreover, they allow for fast and seamless parallel processing, making AI computations a lot faster. Additionally, these chips are usually designed to withstand higher operating temperatures, making it easier to manage thermal dissipation and cooling.
Conclusion
Although cryptocurrency mining and AI computations may seem similar, the requirements for each process are vastly different. Mining chips aren’t suitable for AI computations due to their low parallel processing property, power consumption, size limitations, high latency, and simple algorithms. However, other high-performance chips like ASICs specially designed for artificial intelligence provide faster and reliable performance at a higher cost. When choosing between cryptocurrency mining chips and AI chips, it’s essential to evaluate the specific requirements of the application and decide which chip will provide the best performance and be worth your money.
The world of cryptocurrency mining has been through many changes, and with the shift towards proof of stake, many miners are now left with idle hardware. However, an emerging industry is now capturing the attention of these miners – artificial intelligence.
AI requires the computation of vast quantities of data, and the best tool for such heavy lifting is GPUs. But, GPUs used for cryptocurrency mining are not always ideal for AI training. This is because crypto mining requires GPUs with high hash power, while AI training demands GPUs with abundant vRAM.
Hash power is like the muscle of the operation; it’s all about the number of computations your GPU can crunch per second. The higher the hash rate, the better your odds of unlocking a block and making a crypto fortune. Conversely, vRAM is the capacity for handling and storing vast volumes of data simultaneously. AI training is a notorious data glutton, demanding GPUs to handle and process colossal amounts of data concurrently.
Despite this paradox, GPUs used for crypto mining are not entirely redundant for AI training. GPUs with comparatively lower vRAM can still be employed for training smaller AI models or tasks that don’t require significant vRAM. Entrepreneurs will have to properly study which AI application they focus on.
While this solution may not be as lucrative as crypto mining, it could serve as a viable “plan C.” Some crypto mining companies are already making the switch to AI operations, such as Hive Blockchain and Hut8 Mining.
In conclusion, the shift towards AI has caught the attention of those with idle hardware after the shift towards proof of stake. However, the paradox of GPUs being perfect for one industry, while not ideal for the other, means that entrepreneurs must carefully consider which AI application they focus on. Despite this, GPUs used for crypto mining could still be employed for smaller AI models or tasks that don’t require significant vRAM, making it a viable backup plan if the mining of alternate proof-of-work altcoins seems bleak.
