With the gradual development and popularization of AI artificial intelligence, artificial intelligence has higher and higher requirements for server configuration. If you want to work in the artificial intelligence industry, how to choose a suitable AI artificial intelligence server? Artificial intelligence server configuration selection Different from other industries (games, APP development), if you want to apply artificial intelligence, you must meet the technical requirements of AI.

1. High computing power

The continuous advancement of AI technology cannot be separated from the support of computing power, algorithms and data. AI servers need to bear a lot of computing pressure, which puts forward higher requirements on the processing performance, I/O performance, and storage capacity of the server itself. The CPU can handle basic AI workloads, but deep learning involves multiple large data sets and deploys scalable neural network algorithms. Therefore, AI servers are heterogeneous servers. Combination forms, such as CPU+GPU, CPU+TPU, CPU+other accelerator cards, etc.

2. Storage capacity

The infrastructure has the basis for expanding storage capabilities as the amount of data grows. Deciding which storage technology to adopt depends on many factors, including the level of AI you plan to use and whether you need to make real-time decisions. For example, a fintech company using AI systems for real-time transactions may require fast all-flash storage technology, while for other companies, slower but high-volume storage may be more appropriate. And businesses need to consider how many AI data applications will result. As AI applications generate more data, or as your database grows over time, you need to monitor capacity and plan for expansion.

3. Network infrastructure

Networking is another key component of AI infrastructure. Deep learning algorithms are highly dependent on communication, and as AI technology advances, so does the network. This is why the AI server prefers a highly scalable network. What is the concept of strong scalability? A high-bandwidth, low-latency network environment can ensure consistent service packaging and technology stacks in all regions.

4. Data security

AI may involve handling sensitive data, such as patient records, financial information, and personal data. A data breach is a disaster for any business. Likewise, inputting abnormal data may cause the AI system to make wrong judgments, leading to wrong decisions. AI infrastructure must be fully protected using the latest technologies.