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When purchasing a ThinkView AI computing power all-in-one computer, several factors need to be considered:

Hardware Performance

CPU: The CPU handles various computational tasks, and a multi-core, high-frequency processor can significantly enhance overall system performance. For basic development and learning, mid-to-high-end processors such as Intel i7 or AMD Ryzen 7 are sufficient. For enterprise-level large-scale computations, server-grade processors like AMD EPYC or Intel Xeon Scalable series are recommended.

GPU: Parallel computing capabilities of GPUs are crucial for training and inference processes in deep learning. It is advisable to choose graphics cards that support acceleration technologies such as Tensor Core. Beginners can opt for NVIDIA RTX 3060 or 3070, while mid-to-high-end research and development or enterprise applications may require NVIDIA A100 or higher-performance products.

Memory: The amount of memory directly affects data processing efficiency. Typically, 32GB is recommended, with options for 64GB or 128GB for handling large-scale models. For applications in finance, healthcare, or other fields where data accuracy is critical, systems supporting ECC (Error-Correcting Code) memory are more suitable.

Storage: AI operations involve processing large volumes of data, so high-capacity NVMe SSDs are recommended to accelerate data transfer. For general development and learning, a 1TB SSD is usually sufficient, while enterprise-level large-scale computations should ideally be equipped with SSDs of 4TB or more. RAID arrays can also be used to enhance data redundancy and reliability.

Cooling System: AI computations are energy-intensive and generate significant heat. A robust cooling system ensures hardware stability, preventing performance degradation or hardware damage due to overheating.

Power Supply: When configuring multiple GPUs, a high-power and stable power supply is essential to ensure the system runs smoothly and has a longer lifespan.

AI Functionality and Support

Dedicated AI Chips: Products equipped with NPUs (Neural Processing Units) or other dedicated AI chips can significantly accelerate the execution of AI-specific tasks and enhance the smoothness of human-computer interaction.

Support for Mainstream AI Frameworks: The system should support mainstream deep learning frameworks such as Tensor Flow and PyTorch, as well as commonly used AI development libraries, to facilitate developers' work and expand the range of use cases.

Expandability

GPU Expansion: Multiple GPUs can accelerate the training process in deep learning. If expansion is needed in the future, models with multiple GPU expansion slots should be selected.

Memory and Storage Expansion: To accommodate future needs for handling larger datasets or more complex tasks, the all-in-one computer should allow for memory capacity expansion and support the addition of extra hard drives or SSDs.

Network Capability

For basic needs, it is recommended to have at least dual gigabit Ethernet ports to meet requirements for network segmentation or link aggregation. For applications in edge computing, real-time data processing, or multi-computer cluster scenarios, products with 10-gigabit fibre interfaces or support for 5G module expansion are more suitable.

Brand and After-sales Service

Well-known brands such as ThinkView and Inspur often provide better product quality, system optimisation, and after-sales service. It is advisable to prioritise vendors offering long-term warranties and 24/7 technical support. Quick after-sales response can minimise downtime and losses caused by hardware failures.