I. Core Concept Analysis: Definition and Positioning of Artificial Intelligence Computing and Cloud Services

Specialized computing power hardware

GPU (Graphics Processing Unit) : Due to its proficiency in parallel computing (simultaneously processing large amounts of similar data, such as matrix operations in deep learning), it has become the mainstream hardware for AI training (model development) (such as NVIDIA A100, AMD MI250).

TPU (Tensor Processor) : A chip customized by Google specifically for deep learning, optimizing "tensor operations" (the core computing unit of AI models), suitable for AI inference (practical applications after model deployment, such as real-time recognition by voice assistants).

Other dedicated chips: such as Huawei Ascend AI chips, Cambricon Sygen chips, and low-power chips for edge AI scenarios (such as Horizon Journey series), further reduce the cost and energy consumption of AI computing

AI computing power scheduling capability

For large-scale AI tasks (such as training large models with hundreds of billions of parameters), it is necessary to coordinate the computing power of multiple servers and hundreds of chips. Through "distributed training frameworks" (such as Horovod, Megatron-LM), the computing power can be managed in a clustered manner to avoid the excessively long training cycle caused by insufficient computing power of a single node.

Ii. The Collaborative Model of the Two: How can AI Computing "Go to the Cloud"? The core form is "AI cloud service"

The integration of artificial intelligence computing and cloud services has given rise to the core form of "AI cloud services". Essentially, "cloud service providers encapsulate AI computing power, algorithms, and tools into standardized services, and users can invoke them as needed through apis or consoles." Specifically, it can be divided into three major service models:

I Infrastructure as a Service (AI IaaS) : Providing "on-demand AI computing power"

This is the most fundamental collaborative model. Cloud service providers encapsulate "GPU/TPU clusters" as elastically invoking computing power resources, addressing users' pain points of "high cost and low utilization rate of self-built AI computing power"