The three major elements of deep learning are data, algorithms and computing power. Data is the foundation, algorithms are tools, and computing power is the booster. The improvement of computing power promotes the development of deep learning. Deep learning developed slowly before, except for the algorithm.Server rack cabinet In addition to the reasons, a very important reason is the lack of computing power. The most important support to solve the problem of computing power is the AI server (here mainly refers to the general AI server and GPU server).

What does AI server mean?

An AI server is a data server capable of providing artificial intelligence (AI). It can be used to support local applications and web pages,rack 42u as well as provide complex AI models and services to cloud and local servers. AI servers help provide real-time computing services for various real-time AI applications.

There are two main architectures of AI servers, one is a hybrid architecture that can store data locally, and the other is a cloud platform-based architecture that uses remote storage technology and hybrid cloud storage (a technology that combines local storage and cloud storage) ) for data storage.

From the perspective of the server's hardware architecture, the AI server is a heterogeneous server. In the heterogeneous manner, different combinations can be used according to the scope of the application, such as CPU+GPU,server rack server CPU+TPU, CPU+other accelerator cards, etc.

We all know that ordinary servers use CPU as the provider of computing power, adopt a serial architecture, and are good at logical calculations, floating point calculations, etc. Because a large amount of branch jump processing is required when making logical judgments, the structure of the CPU is complex, and the improvement of computing power is mainly achieved by stacking more cores.

However, with the application of network technologies such as big data, cloud computing, artificial intelligence and the Internet of Things, the data flooding the Internet has increased exponentially, which has posed a serious test to traditional services with CPU as the main source of computing power, and The current CPU manufacturing process and the number of cores in a single CPU are close to the limit, but the increase in data continues, so the data processing capabilities of the server must be improved. Therefore, in this environment, AI servers emerged as the times require.

AI servers on the market now generally adopt the form of CPU+GPU, because GPU, unlike CPU, adopts a parallel computing model and is good at sorting out intensive data operations, such as graphics rendering, machine learning, etc. On the GPU, NVIDIA has a clear advantage. The number of cores on a single GPU card can reach nearly a thousand. For example, if equipped with 16 NVIDIA Tesla V100 Tensor Core 32GB GPUs, the number of cores can exceed 10,240, and the computing performance can reach up to 2 petaflops. . And after years of market development, it has been confirmed that CPU+GPU heterogeneous servers do have a lot of room for development in the current environment.

AI servers can use a variety of model architectures, such as neural networks, decision trees, support vector machines, etc., to perform complex AI model calculations. They can support a variety of commonly used AI technologies, such as machine learning, natural language processing, computer vision, biological information analysis, etc. The AI server can also run specific AI applications, automatically recognize images and text, adjust them as needed, or train computing models, etc.

The flexibility and scalability of AI servers make them very effective in supporting and running a variety of different AI technologies. They can provide efficient storage and computing capabilities and can meet the big data needs of different environments. AI servers can also help people quickly access and process data to more quickly improve key AI performance indicators in research and business processes.

AI servers have excellent graphics processing capabilities and high-performance computing capabilities. Compared with ordinary servers, there is no difference in memory, storage, and network. The main reason is that big data, cloud computing, artificial intelligence, etc. require greater internal and external resources. Storage to meet the collection and organization of various data.

What is the difference between AI servers and ordinary servers?

From the perspective of the server's hardware architecture, the AI server is a heterogeneous server. In the heterogeneous manner, different combinations can be used according to the scope of the application, such as CPU+GPU, CPU+TPU, CPU+other accelerator cards, etc. Compared with ordinary servers, there is no difference in memory, storage, and network. The main reason is that big data, cloud computing, artificial intelligence, etc. require larger internal and external memory to meet the collection and organization of various data.

The number of cards is inconsistent: Ordinary GPU servers usually have single or dual cards. AI servers need to undertake a large amount of calculations and are generally configured with more than four GPU cards, or even build an AI server cluster.

Unique design: Since the AI server has multiple GPU cards, it requires specialized design of the system structure, heat dissipation, topology, etc. to meet the long-term stable operation requirements of the AI server.