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Problem
Why building and operating
AI computing infrastructure is challenging.
Lack of clear benchmarks for appropriate investment scale.
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The computing resources required for AI models vary significantly depending on their type and characteristics.
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To operate LLMs effectively, technologies such as distributed training, model parallelism, and efficient inference techniques are required.
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MMLMs involve complex processes such as data preprocessing, feature extraction, and fusion, requiring not only high-performance computing resources but also efficient data processing and management systems.
Difficulty in performance optimization.
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While the latest AI accelerators deliver high performance, they also generate significant heat and consume large amounts of power.
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Operating next-generation AI accelerators requires comprehensive support for data center infrastructure, including providing a stable and efficient AI system environment and delivering end-to-end solutions that take the data center’s physical environment into account.
Difficulty in selecting
AI accelerators.
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AI accelerators are hardware components specialized for improving AI computation performance, and include various types such as GPUs, NPUs, and TPUs.
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Each accelerator has characteristics better suited to specific AI workloads, making it challenging to select the most appropriate accelerator based on task requirements.
Scalability of
AI data center architecture.
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The latest AI accelerators deliver high performance but also generate significant heat and consume substantial power.
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Operating next-generation AI accelerators requires comprehensive data center infrastructure support, including a stable and efficient AI operating environment and end-to-end solutions that account for the physical conditions of the data center.
Service
Optimization services
DIA NEXUS focuses on.
Infrastructure
sizing
By comprehensively analyzing AI model types, workload characteristics, and data scale, we propose optimal infrastructure performance and capacity.
Infrastructure
sizing
By comprehensively analyzing AI model types, workload characteristics, and data scale, we propose optimal infrastructure performance and capacity.
AI accelerator
recommendations
By analyzing AI workload characteristics (such as training and inference), we recommend optimal AI accelerators to maximize AI system performance.
Data center
recommendations
We support stable and efficient AI system operations by providing data center facility guidelines that consider the characteristics of the latest AI accelerators.