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The Impact of 800G Transceivers on AI Computing

Author Leslie

Date 05/29/2024

This article tells you what AI computing is and the impact of 800G transceivers on AI computing. AI is increasingly used in today's society, and early deployment of high-speed equipment helps business success.

AI computing is becoming increasingly important with the development of current technology, and more companies are choosing to design and train their own AI models. Behind it, the speed of data transmission has become the focus. Traditional networks cannot meet the bandwidth and latency requirements of Artificial Intelligence computing, so to meet the needs of it, 800G transceivers are being used in large quantities. This article will show you the impact of high-speed modules on AI computing and what they do.

What is AI Computing

Artificial intelligence computing involves computer systems mimicking human intelligence processes, such as learning, reasoning, problem-solving, and decision-making. AI computing involves model training, which requires training AI through designing algorithms and a large amount of data so that it can perform complex tasks. For example, image recognition, autonomous driving, and helping questioners solve questions.

The Role of Artificial Intelligence Computing

AI technology has been applied to many fields, including but not limited to transportation, manufacturing, medicine, finance, and other industries. For example, in autonomous driving, AI uses AI computing to analyze road conditions and surrounding vehicle behaviors in real time and make correct response actions in a very short reaction time to achieve autonomous driving of the car. During the analysis and response, Artificial Intelligence computing analyzes and processes the road conditions through cameras and draws the best conclusion. This moment requires the transmission and analysis of a large amount of data to ensure the safety of autonomous driving.

Limitations of AI

Although AI has achieved remarkable results in many fields, it still faces many challenges. For example, the transmission rate of traditional networks is not enough to further support the huge data of AI computing. Although deploying a large number of 100G modules can temporarily meet the needs, their energy consumption is very high and the maintenance cost increases significantly. Therefore, how to solve the transmission rate, increase network bandwidth, and solve the energy consumption problem has become a limitation for the development of AI. To solve the problem of AI computing, equipment vendors have begun to develop optical modules with higher speeds. For example, 400G and 800G transceivers. Compared with 100G modules, their transmission rates have increased several times. Faster transmission rates bring higher bandwidth and lower latency. Next, let's take a look at how 800G modules help AI computing solve its problems.

What is an 800G Transceiver

Before understanding how to solve the problems of AI computing, let's first understand what 800G optical transceivers are. As the name suggests, 800g optical transceivers are optical modules with a transmission rate of up to 800Gbps. They have three types: QSFP-DD, OSFP, and CFP8. The mainstream packages on the market are QSFP-DD and OSFP. The advantage of QSFP-DD is that it is compatible with packages such as QSFP28 and QSFP56, reducing the cost of port and device deployment. OSFP adopts an eight-channel design, which can provide higher data transmission rates and longer transmission distances. In addition, OSFP has a better heat dissipation design than QSFP-DD, and its size is larger than QSFP-DD, which may support higher rates in the future.

How to Solve AI Computing Limitations

In terms of transmission rate, 800G transceivers increase the transmission speed by 8 times compared to traditional 100G modules. This means that it can provide lower latency and higher network bandwidth, making AI computing more responsive and accurate.

 

Traditional 100G modules can also achieve the effect of 800G optical transceivers through large-scale deployment, and it seems to be less expensive. However, the power consumption of 8 100G modules is twice or even higher than that of 1 800G optical module. In the long run, 800G transceivers consume less power and have lower heat dissipation costs. Deploying 800G optical modules can reduce maintenance costs and have higher scalability.

 

In addition to increasing transmission rates and reducing power consumption, 800G optical transceivers can also optimize network architecture. When using multiple 100G modules, more optical fibers are also needed to interconnect devices. The 800G optical module only requires one pair of optical fibers and modules to achieve the effect of 8 pairs of 100G modules and 8 pairs of optical fibers, simplifying wiring, optimizing network architecture, and making later maintenance more convenient.

 

Finally, with the continuous development of AI technology, the required bandwidth and transmission rate are also increasing, and new AI applications are constantly emerging, such as autonomous driving and Chatgpt. These applications require larger-scale and more complex computing and data processing, and traditional networks cannot cope with AI upgrades. Deploying 800 Goptical modules can more easily cope with future AI computing updates and the popularization of new applications.

Other 800G Product

Of course, in addition to using 800G transceivers, 800G DAC is undoubtedly a better alternative option in short-distance transmission. It has almost no power consumption and is more economical than 800G optical modules. In short-distance connections across racks, 800G DAC is the best choice.

AI Data Center Network Architecture

In AI data centers, as AI models are widely used in various industries, traditional network architectures cannot meet the bandwidth and latency requirements of large-scale cluster training. Distributed training of large models requires communication between GPUs, which increases east-west traffic on the AI ​​data center network architecture. AI computing uses huge amounts of data for training, which inevitably leads to reduced network latency and training performance in traditional network architectures. Therefore, to meet the needs of AI training, Fat-Tree networks have emerged.

In traditional networks, bandwidth gradually converges from leaf nodes to the root, and its bandwidth exceeds the total bandwidth of the trunk network, which will cause network congestion and increased latency. In the Fat-tree network, the bandwidth of its leaf nodes gradually increases to the root, and the bandwidth of the trunk part is balanced with the bandwidth of the leaf nodes. This improves network efficiency and accelerates the AI ​​computing process. Compared with traditional networks, Fat-tree networks have better bandwidth utilization efficiency and avoid network bottlenecks and congestion.

Conclusion

As a cutting-edge technology in the new era, 800G transceivers provide the underlying support for AI computing, making AI technology possible. It is a necessary technology not only for the present but also for the future. By deploying 800G optical transceivers and devices, AI model training can help companies achieve further development. If you have any questions about Artificial Intelligence computing, please feel free to please feel free to contact QSFPTEK's CCIE/HCIE engineers at support@qsfptek.com. Our engineers will provide you with comprehensive support.

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