en.Wedoany.com Reported - On June 4, Beijing Parallel Technology Co., Ltd. announced that the company plans to purchase GPU computing servers and memory, with the total purchase amount expected to not exceed 81.6335 million yuan. The procurement includes two categories of assets: GPU computing servers and memory. Upon completion of the transaction, these assets will be used to expand the company's own computing resource pool.
According to the announcement, Parallel Technology intends to purchase GPU computing servers from Lianchuang Wantong (Beijing) Intelligent Computing Technology Services Co., Ltd., Inner Mongolia New Dongji Tai Technology Co., Ltd., and Shandong Zhengyun Information Technology Co., Ltd., with the procurement contract amount expected to not exceed 56.602 million yuan. At the same time, the company plans to purchase memory from Shenzhen Huapengfei Supply Chain Management Co., Ltd., with the procurement contract amount expected to not exceed 25.0315 million yuan. The total amount for both procurements does not exceed 81.6335 million yuan. The company's announcement also indicates that this asset purchase does not constitute a major asset restructuring nor a related-party transaction, and is an operational procurement arrangement centered around the company's main business. For computing service enterprises, server and memory procurement directly impacts the scale of deliverable computing power, resource pool scheduling capabilities, and the ability to provide continuous services to customers. This is especially critical against the backdrop of growing demand for artificial intelligence training, inference, high-performance computing, and industrial simulation. The expansion of proprietary computing resources will affect the maximum scale of projects the platform can undertake and the stability of delivery.
Parallel Technology primarily provides computing services for scenarios such as supercomputing cloud, intelligent computing cloud, design simulation cloud, and AI cloud. According to the company's official website, its product portfolio includes GPU cloud servers, GPU high-performance computing pools, bare metal servers, GPU container cloud, large model MaaS platform, Parallel Supercomputing Cloud, and Parallel Intelligent Computing Cloud.
The core of this procurement is not merely supplementing individual devices, but rather continuing to strengthen the underlying assets around the computing resource pool. GPU computing servers are used to support model training, inference services, scientific computing, engineering simulation, and industry AI applications. Memory configuration directly affects the stable operation of multi-task concurrency, data processing throughput, and large-scale computing tasks. As enterprise customers shift from one-time computing rentals to long-term, project-based, and platform-based usage, computing service providers need to maintain alignment between hardware scale, network interconnection, storage, scheduling systems, and operational capabilities. If server expansion outpaces scheduling and operational capabilities, resource utilization may come under pressure; conversely, insufficient underlying resources will limit the platform's response speed in AI and high-performance computing projects. Parallel Technology's inclusion of both GPU servers and memory in this asset purchase indicates that the company continues to invest at the infrastructure level to enhance computing supply capabilities.
From an industry environment perspective, China's computing service market is transitioning from early-stage resource leasing to a model of "computing resource pool + scheduling platform + industry application services." Research institutions, universities, industrial software companies, AI development teams, and manufacturing enterprises are continuously releasing demand for elastic computing, hybrid cloud computing, model training environments, and engineering simulation resources. While the procurement amount for Parallel Technology is not particularly high compared to the scale of large intelligent computing center construction, for computing service providers targeting niche customer segments, equipment upgrades and resource pool expansion remain crucial for maintaining business capacity. The key variable going forward will be whether the company can translate the newly added assets into higher resource utilization, customer repeat purchases, and industry scenario orders, rather than merely achieving hardware scale expansion.
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