The release of ChatGPT 4.0 has ushered in a new era of artificial intelligence, offering a glimpse into the realization of science fiction in the real world. Currently, AIGC (Artificial Intelligence General Computation) has become the battleground for the industrialization of AI, and as the number of large-scale model types continues to grow, data storage is emerging as one of the bottlenecks hindering the practical application of AI.

ChatGPT encompasses various applications such as text generation, speech synthesis, image generation, video generation, code generation, and virtual human generation. Behind each of these applications lies the process of data collection, standardization, training, inference, and archiving, involving the entire industry ecosystem. The storage time required for training models determines the production time of large models, and it demands servers that can provide faster speeds, greater reliability, simpler management, and the ability to scale flexibly. Some liken the process of training large model data to "alchemy," where GPU computing power is akin to firewood. As long as the GPU firepower is strong and data ingredients are sufficient, large models can be quickly produced. While this analogy has its merits, it overlooks the difficulties and challenges associated with storing the raw materials, which are the data.

• Firstly, there's the challenge of heterogeneous data storage. With the increasing scale of large models, the traditional single data storage mode is insufficient to meet the demands of multimodal forms. Large models encompass not only text but also include image, audio, and video data, resulting in a multi-source and heterogeneous data landscape. Large models rely more on GPU computing than CPU computing, necessitating storage that can adapt to GPU storage acceleration.
• The storage capacity in the order of exabytes (EB): The precision of large models relies on the scale of data feeding, as data serves as the fuel for large model algorithms. The broader the sources of training data, covering a wide variety of categories, determines the depth and connectivity of large models. Furthermore, the entire process of collecting, annotating, training, and inferring with large model data requires repeated data copying within the data storage pipeline. Conventional server processing efficiency falls short of meeting the demands of AIGC's large model applications. Hence, there's a need for servers that can scale elastically and provide stable storage to ensure reliable model training.
• High storage density and high-speed I/O: During the training of large models, which often involves massive computations and data processing, the I/O characteristics become complex. Comprehensive storage capabilities are required: frequent token retrieval from the dataset can easily lead to high-concurrency, massive I/O, necessitating low latency to ensure performance. Additionally, when storing model checkpoints, large bandwidth is required to support rapid data writes.

Gooxi's Purley platform 4U36-disk servers offer numerous advantages such as high-capacity nodes, extreme reliability, and stability. They are suitable for data collection, data quality processing, data archiving, and are applicable in various AI data storage scenarios, including medical research, AI art, and autonomous driving.

Gooxi's AMD dual-socket standard servers boast high performance, high reliability, and flexible expansion capabilities. They have rapid data transfer and processing capabilities, fully meeting storage requirements across AI, virtualization, and database use cases. Moreover, they can significantly enhance performance, reliability, and functionality through software and hardware co-upgrades, leveraging innovative distributed storage architecture for intelligent I/O optimization and tapping into the system's performance potential. They are highly compatible and scalable, accommodating various common AIGC software and hardware platforms, making integration and expansion easy to meet different customer needs.

Furthermore, Gooxi, drawing on its years of practical experience in the server field, has introduced a distributed storage system capable of providing extensive storage space to meet AIGC applications' big data needs. Additionally, distributed storage offers high-speed read and write performance to support real-time requirements. With high availability and scalability, based on distributed storage, it ensures the stable operation of AIGC applications, facilitating large-scale deployment. As the AIGC era unfolds, the generation and processing of data will become increasingly dispersed, driving the convergence and evolution of distributed storage systems.

With the advent of the AIGC era, Gooxi will continue to closely monitor the industry's development and, by building a reliable data storage platform, better address the storage challenges of the AIGC era, accelerating the commercialization of AI.