Driven by the wave of large models, training and inference workloads are growing exponentially. Traditional general-purpose servers are inefficient at handling AI workloads such as Transformers and recommendation systems, creating a huge gap in cluster computing power and directly slowing down product iteration and intelligent upgrades.
SOLUTION INTRODUCTION
Solution introduction
Built around high-performance AI servers, this solution creates a full-stack intelligent infrastructure spanning cloud, network, and edge. With extreme compute density, an agile elastic architecture, and full-lifecycle AI operations, it helps customers turn AI into core productivity, significantly reduce TCO, and accelerate business innovation and time-to-market.Siloed hardware construction makes computing resources difficult to share, resulting in low overall utilization. At the same time, server clusters at the scale of tens of thousands of accelerator cards cause costs to surge, squeezing profit margins.
In data centers with tens of thousands of nodes, manual inspection and fault handling are extremely inefficient. Chaotic firmware versions and a lack of fault prediction lead to long mean time to repair. O&M teams are constantly firefighting and cannot focus on business optimization.
SOLUTION ADVANTAGES
Solution advantages
The following points are replaceable demonstration content based on the current solution structure.Extreme AI performance unleashes innovation potential
Servers are specially enhanced for high-throughput memory, ultra-high-bandwidth interconnect, and intelligent cooling. Measured AI training and inference performance is several times higher than traditional architectures, shortening the launch cycle of new models and applications from months to weeks.
Green high-density computing significantly optimizes TCO
Supports cold-plate and immersion liquid cooling, with per-rack power density exceeding 40 kW and PUE below 1.2. Under the same computing power demand, total data center energy consumption is reduced by more than 40%, effectively controlling electricity and space costs and delivering on green low-carbon commitments.
Agile open architecture supports smooth evolution
Modular decoupled design, compatible with mainstream AI frameworks, cloud-native ecosystems, and third-party accelerator cards. Supports non-disruptive scaling from small inference clusters to 10,000-card training clusters, protecting existing investments and enabling infrastructure to continuously evolve as AI technology advances.
SOLUTION ARCHITECTURE
Solution architecture
This staged architecture visualizes the current planning path and is not a delivery commitment.- 01
Build a full-stack AI computing foundation
Deploy AI training and inference servers equipped with the latest GPUs/NPUs at scale in core data centers. Build high-speed interconnected clusters at the scale of thousands or even tens of thousands of accelerator cards to provide native, massive computing power for large models ranging from tens of billions to trillions of parameters.
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Enable ubiquitous cloud-edge collaborative deployment
Deploy edge AI servers on demand at 5G MEC, CDN, and edge cloud nodes, extending intelligent computing power to the network edge. Achieve millisecond-level response and real-time local data processing to support scenarios such as video stream analysis and autonomous driving.
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Introduce AI-driven intelligent elastic scheduling
Deploy an intelligent computing scheduling platform that uses AI prediction algorithms to analyze workload trends. Automatically perform minute-level scaling of server resources and optimal task matching to improve resource utilization and achieve a dynamic balance between cost and performance.
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Implement full-lifecycle AI operations
Integrate BMC out-of-band management with an AI operations platform. Apply machine learning to massive sensor data to accurately predict potential failures of components such as disks and memory, automatically trigger firmware upgrades and preventive replacement, and transform operations from reactive firefighting to proactive immunity.
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