Products Knowledge

The differences in command execution between ThinkView AI Compute All-in-One PCs and ordinary all-in-one PCs are mainly reflected in the following aspects:

Performance and Efficiency Differences: ThinkView AI Compute All-in-One PCs are equipped with high-performance GPUs, more processor cores, larger memory, and higher - bandwidth storage, making them suitable for executing commands that require AI - based calculations. For instance, the MINI Cube AI All - in - One PC boasts a computational power of 270TOPS. When running a large - scale model with 32 billion parameters, its inference speed is more than twice as fast as that of the Apple Mac Studio. Ordinary all - in - one PCs are sufficient for handling simple instructions related to routine office work and entertainment. However, when it comes to processing complex AI-based instructions, they tend to be slow and may even fail to complete the task.

Support for AI- Specific Instructions: ThinkView AI Compute All-in One PCs are generally optimized for AI - based frameworks and algorithms. They are equipped with compilers tailored to specific algorithms and efficient memory-scheduling systems, enabling them to efficiently execute instructions related to machine learning, deep-learning training, and inference. In contrast, ordinary all-in-one PCs lack such specific optimizations. When directly executing AI - based instructions, they may encounter compatibility issues. Even if they are compatible, the hardware resources may not be fully utilized due to poor scheduling.

Parallel Processing Capability: When executing multi-tasking or complex task -related instructions, ThinkView AI Compute All-in-One PCs can leverage their powerful parallel - processing capabilities to handle a large number of data or sub - task instructions simultaneously. For example, when performing image - recognition tasks, they can conduct parallel calculations of image features across multiple regions. Ordinary all-in-one PCs have weaker parallel- processing capabilities. When executing multi - tasking or complex instructions, they are prone to lagging and can only prioritize key tasks, processing other tasks sequentially.

Advantages of Localized Instruction Processing: Most AI Compute All-in-One PCs support localized AI - based task execution, eliminating the need for cloud-based processing. For example, the Office Cube helps hospital doctors use voice commands to locally and instantly retrieve medical records and generate diagnostic reports. This approach avoids the latency and privacy risks associated with cloud - based data interactions. In contrast, ordinary all - in - one PCs often rely on cloud services to process complex AI - based instructions that exceed their local capabilities. When there is no network connection or the network is poor, instruction processing is likely to be interrupted, and the speed is greatly affected.