Benchmarking Co-Pilot PCs' Performance on Local AI Workloads

July 10, 2024 Hardware

Artificial Intelligence (AI) has been making remarkable strides in various sectors, from healthcare to finance, and now, it's gradually creeping into our computers. GitHub Copilot, a new AI tool developed by GitHub and OpenAI, is a case in point. Its primary aim is to assist programmers by providing suggestions for code completion. Given the potential of such AI tools, it becomes crucial to understand how well typical PCs can handle local AI workloads. An investigation by Tom's Hardware provides insightful details on this subject.

As AI continues to evolve, machines are being tasked with more complex assignments that require substantial computing power. The GitHub Copilot, for instance, operates on a language model known as Codex, trained on a diverse range of public internet text. However, despite its sophisticated learning model, it doesn't require a high-end PC or supercomputer to operate. Instead, it works on cloud-based servers, leaving the local machine with the task of running the Visual Studio Code editor and communicating with the GitHub Copilot service.

The study by Tom's Hardware aimed to determine whether AI applications like GitHub Copilot would affect a PC's performance. To carry out the test, they used a variety of computers, ranging from high-end desktops to laptops and Raspberry Pi. They used the Visual Studio Code editor, running GitHub Copilot, to mimic the demands of a typical AI workload.

The results were fascinating. The high-end desktops handled the AI workload smoothly, with only minor CPU usage spikes during the initial setup. On the other hand, laptops and Raspberry Pi showed a more noticeable impact on performance. Their CPU usage and memory usage shot up, especially during the initial setup, but returned to normal levels once the setup was complete.

The findings highlighted that while high-end PCs can handle AI workloads with ease, more modest setups like laptops and Raspberry Pi might struggle. However, this is only during the initial setup phase. Once the AI tool is up and running, even these modest setups cope well.

In conclusion, the future of AI on local machines looks promising. The impact on performance is manageable, even on less powerful machines. As AI tools like GitHub Copilot become more prevalent, it will be interesting to see how hardware manufacturers respond to the demand for more AI-friendly setups. It seems clear that the integration of AI with everyday computing is not only possible but also imminent. However, the degree to which our machines will need to adapt to handle AI workloads effectively remains to be seen.

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