AI Large Model Solution
Through large-scale, high-performance, highly integrated super intelligent computing power clusters, it responds to the computing power, network, and storage needs required for AI large model training and reasoning, and combines with efficient computing power scheduling mechanisms to provide highly efficient and cost-effective computing power solutions for AI large model research and development enterprises.
Massive computing power demand
Large AI models require a large amount of computing power for training and inference, and these models usually have billions or even hundreds of billions of parameters, involving large-scale matrix operations and parameter updates, which require a large amount of computing power to handle, and have very high requirements for the scale and performance of hardware devices and computing platforms.
Large amount of data interaction
AI large model training will use large-scale training datasets, usually involving hundreds of billions or even trillions of vocabularies, the training process generates a huge amount of parameters, gradients and intermediate computation results, requiring a large amount of memory and storage space for storage, while the performance requirements for storage are extremely high.
Distributed training support
In order to accelerate the training of large models, it is usually necessary to rely on distributed parallel computing to accelerate the distributed training process needs to synchronize the model weight parameters and a large number of temporary variables generated during the training process, requiring the communication network between the nodes to have a very high throughput and load balancing capabilities.
High cluster stability requirements
As the scale of large model training increases, the required training time also increases gradually, which requires the computing cluster to run under full load for a long period of time, which requires high requirements for the cluster’s architectural design and operation and maintenance capabilities.
The combination of high-performance GPU hardware, all-flash storage, and RDMA high-speed networking effectively helps customers accelerate large model training.
Efficient and easy to use
Cloud-native automated deployment capability allows users to easily submit, schedule, and monitor distributed training tasks, improving the efficiency and accuracy of task execution.
Stable and Reliable
Relying on rich cloud operation and maintenance experience and architecture optimization design, combined with the distributed training framework, it dynamically adjusts the computing power in response to changes in computing power demand, hardware failures and other scenarios to ensure stable operation of the task while accelerating the training of large models to the maximum extent.
By analyzing factors such as task type, task resource demand, computing power resource status, and regional computing power characteristics, we dynamically adjust the allocation and utilization of computing power resources to provide customers with more cost-effective computing power resources that meet their needs.
AI Large Model Architecture
Provide a variety of GPU computing power resources, including A100, A800, etc., combined with high-performance storage, high-speed interconnection network, to meet the computing power needs of large model training scenarios.