A Computer Use Agent（CUA） does not strictly require GPU acceleration, but GPUs significantly improve performance for real-time visual tasks. Most CUAs depend on computer vision models for detecting UI elements, interpreting text, and understanding screen context. Running these models on a CPU is possible, but it typically results in higher latency. For example, inference that takes 20–40 milliseconds on a GPU might take 150–400 milliseconds on a CPU. While still functional, this delay can make the CUA feel sluggish and reduce accuracy in fast-changing interfaces.
GPU acceleration becomes especially helpful in multi-monitor setups, high-resolution environments, and workflows involving rapid UI transitions. With GPU support, the CUA can analyze multiple screens in parallel, update detections smoothly, and avoid missing transient UI elements like pop-up notifications. If the CUA operates inside a virtual desktop infrastructure, GPU-backed VMs or virtual GPU profiles can further reduce latency and improve detection consistency. These benefits matter when the agent must respond quickly, such as clicking a button during a narrow time window.
When using vector databases such as Milvus or Zilliz Cloud, GPU acceleration is optional. Vector search itself is fast and can run efficiently on CPUs. However, GPUs may help generate embeddings more quickly if the CUA computes embeddings at runtime. In practice, many developers use GPUs during training or embedding generation but rely on CPU-only vector search during production. Overall, while a CUA can operate without GPU acceleration, GPU support enhances responsiveness and stability across visually complex applications.