As AI transitions from an "optional" to a "mandatory" component, enterprises are accelerating the construction of their own computing infrastructure—whether large groups or small businesses are carefully evaluating: should they use cloud API or build their own local AI workstations? Recent trends show that more companies are choosing the latter, especially in scenarios requiring high data security, cost control, and long-term business stability. In such cases, investing in self-built AI hardware can typically recoup costs within 1.5 to 2.5 years, making its economic advantages increasingly evident.

Depending on the complexity of the task, enterprises need to match different scale models and hardware configurations:

- 7B parameter model: suitable for light tasks such as basic text generation and customer service Q&A, recommended with an entry-level GPU (such as RTX 4090) paired with 64GB of memory and a high-speed NVMe SSD;

- 13B parameter model: capable of handling multi-turn dialogues, logical reasoning, and simple code generation, requires dual GPUs or professional graphics cards (such as A6000), with a suggested memory of 128GB or more;

- 70B-scale large model: suitable for deep analysis, scientific simulation, or enterprise-level agent deployment, must adopt a multi-GPU server architecture (such as 8×A100/H100), equipped with TB-level memory and high-bandwidth storage systems.

It is worth noting that GPUs are not the only key factor. Memory capacity and bandwidth, hard disk I/O performance, power stability, and cooling efficiency collectively determine whether the system can run efficiently over the long term. Experts emphasize: "The performance bottleneck often appears at the weakest link"—for example, a high-speed GPU paired with a low-speed hard drive will significantly slow down model loading and inference speed. Therefore, balanced configuration is more important than simply stacking top-tier components.

In this context, Kingston Technology has launched a full-stack hardware solution tailored for enterprise AI scenarios, including high-performance DDR5 memory, enterprise-grade NVMe solid-state drives, and customized storage architectures, emphasizing high reliability, long-term supply assurance, and professional technical support, helping enterprises avoid common pitfalls such as "buying expensive GPUs but having unstable systems."

For small and medium-sized enterprises, building their own AI workstation is not just a technological upgrade, but also a demonstration of strategic autonomy: it avoids uploading sensitive data to public clouds and allows flexible iteration of private models. At present, with fluctuations in the global computing power supply chain, local deployment highlights its resilience value.

As AI enters an era where "application is king," the rational choice of computing infrastructure has become the first dividing line for enterprises' intelligent transformation.