Overlooking the Misconception That a Beastly PC is Necessary for Local AI Operations - My Seven-Year-Old Mid-tier Laptop Disproves the Notion
In a recent series of tests, it was demonstrated that even older laptops, like a 7-year-old Huawei MateBook D, can be used for AI applications, although with some limitations.
The tests were conducted using three specific AI models: gemma3:1b, llama3.2:1b, and deepseek-r1:1.5b. Despite the laptop's limited hardware resources, these models performed similarly to what one might expect on more powerful hardware.
However, it's important to note that older laptops will not perform as well as newer ones when running AI applications. The MateBook D, for example, sports an older-generation Intel CPU, integrated graphics, and around 8-16 GB RAM, which is considerably less than what is recommended for modern AI workloads.
As a result, AI responses may be generated more slowly and less fluidly, affecting usability, especially for interactive or real-time applications. Larger or more complex AI models will also likely be unviable to run locally on such hardware.
To overcome these challenges, using cloud-based AI inference with the laptop acting as a client can enable effective AI use without hardware constraints.
In practice, the 7-year-old MateBook D was able to churn out AI responses at around 10 tokens per second for gemma3 and Llama 3.2, while Deepseek r1 was slightly slower at around 8 tokens per second.
Despite these limitations, the MateBook D was able to complete tasks significantly faster than human typing speed. It's worth mentioning that the tests were done purely using the CPU and RAM, and the laptop harvested a few GB of RAM for the iGPU to use, but Ollama, the AI tool used in the tests, is not officially supported by the laptop's hardware.
It's also noteworthy that Ollama, a platform-agnostic AI tool, can be used on Linux, Mac, and Windows systems. Examples of running AI on less powerful hardware can be found on platforms like YouTube, such as running AI on a Raspberry Pi and home servers made up of older and cheaper hardware.
While not ideal, a 7-year-old Huawei MateBook D can potentially run small AI models locally with tools like Ollama, but with notable performance and responsiveness limits. Upgrading to a modern laptop with a multi-core i7 CPU, fast memory, and dedicated or efficient integrated GPU greatly improves the experience and feasibility of running AI applications.
In conclusion, running AI locally on a PC does not necessarily require high-end hardware. Older laptops can still be used for AI applications, albeit with some limitations. However, upgrading to a more powerful laptop can significantly improve the user experience and the feasibility of running AI tools.
[1] Modern Laptop Specifications [2] AI-Friendly Laptop Recommendations
- With an upgrade, a modern laptop boasting a multi-core i7 CPU, fast memory, and either a dedicated or efficient integrated GPU would offer a significantly improved experience for running AI applications.
- The test results on a 7-year-old Huawei MateBook D demonstrated that small AI models could be run locally with tools like Ollama, although with performance and responsiveness limitations.
- As technology advances, examples of AI applications running on less powerful hardware, such as home servers compiled from older, cheaper hardware, can be found across platforms like YouTube.
- To work around hardware limitations, cloud-based AI inference can allow for effective AI usage on older laptacts, with the laptop functioning as a client device.
- The AI responses generated by the MateBook D were slightly slower than those produced by newer, more powerful laptops, but still surpassed human typing speed in completing tasks.
- Windows, being one of the supported systems for Ollama, joins Linux and Mac as platforms where AI applications can be utilized, expanding the reach of AI into various aspects of lifestyle, from office work to gaming, home-and-garden, and even artistic endeavors made possible by artificial intelligence.
