Training AI models is a special challenge. Developing basic Large Language Models (LLMs) such as Llama 3.1 and GPT 4.0 requires a significant budget and resources, which only a few large enterprises in the world can achieve. These LLMs have billions to trillions of sets of parameters that require adjustments to the complex data center switching matrix in order to complete training within a reasonable job completion time.

For many businesses, investing in AI requires a fresh approach: leveraging their own data to refine these foundational LLMs, solve specific business problems, or provide deeper customer engagement. However, with the popularization of AI, enterprises hope to use new methods to optimize AI investments, thereby improving data privacy and service differentiation.

For most people, this means transferring some of their internal AI workloads to private data centers. The current popular debate between “public cloud and private cloud” data centers also applies to AI data centers. Many companies are intimidated by new projects such as building AI infrastructure. Challenges do exist, but they are not insurmountable. The existing knowledge of data centers is not outdated. All you need is some help, Zhanbo Network can provide guidance for you.In this blog series, we will explore the different considerations that businesses have when investing in AI, and how Juniper Networks’ “AI Data Center ABC” drives different approaches: application (A), build (B) vs. purchase (B), and cost (C).

It would be helpful to have a better understanding of infrastructure options, some basic principles of AI architecture, and the fundamental categories of AI development, delivery, training, and inference.

The inference server is hosted in the front-end data center connected to the Internet, where users and devices can query fully trained AI applications (such as Llama 3). Using TCP, inference queries and traffic patterns are similar to other cloud hosting workloads. The inference server can perform real-time inference using a regular computer processing unit (CPU) or the same graphics processing unit (GPU) used for training, providing the fastest response speed with the lowest latency, typically measured by metrics such as “time to reach the first token” and “time to reach incremental tokens”. Essentially, this is the speed at which LLM responds to queries, and if the scale is large, it may require significant investment and expertise to ensure consistent performance.

On the other hand, training has unique processing challenges that require special data center architectures. The training is conducted in the back-end data center, where the LLM and training data set are isolated from the “malicious” Internet. These data centers are designed with high-capacity, high-performance GPU computing and storage platforms, and use dedicated rail optimized switching matrices interconnected with 400Gbps and 800Gbps networks. Due to the large number of “elephant” streams and extensive GPU to GPU communication, these networks must be optimized to handle the capacity, traffic patterns, and traffic management requirements of continuous training cycles that may take months to complete.

The time required to complete the training depends on the complexity of the LLM, the number of neural network layers used to train the LLM, the number of parameters that must be adjusted to improve accuracy, and the design of the data center infrastructure.But what is a neural network and which parameters can improve LLM results?

Neural network is a computing architecture designed to mimic the computational model of the human brain. A neural network consists of a set of progressive functional layers, where the input layer is responsible for receiving data, the output layer is responsible for presenting results, and the intermediate hidden layer is responsible for processing raw data input into usable information. The output of one layer becomes the input of another layer, so that queries can be systematically decomposed, analyzed, and processed on each set of neural nodes (or mathematical functions) until results are obtained.

Each neural node within each layer has a neural network connection network structure, and AI scientists can apply weights to each connection. Each weight is a numerical value representing the strength of association with a specific connection.

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