Running the Same GUI Agent on Different Hardware: What Changes?

When we talk about running AI locally, the first questions are usually about model size, memory, and tokens per second.

Those numbers matter. But they do not tell the whole story for a GUI agent.

A GUI agent does not receive one prompt and return one answer. It repeatedly observes a screen, reasons about the current state, performs an action, and observes the result. A task may involve many such cycles. That makes the device, inference runtime, and interaction loop part of one system.

This is one of the things we have been thinking about while working with Mano-P, our open-source, vision-driven GUI agent.

A GUI agent has a different workload from chat

A typical chat request has text input followed by generated text. A GUI agent may process a screenshot at every step, along with the task instruction and the history of previous actions. Its output, on the other hand, may be only a click, a keystroke, or a scroll action.

That changes what we should measure.

Decode tokens per second is not enough. We also need to look at prefill latency at realistic context lengths, end-to-end time per interaction, total task time, and task completion rate.

A small delay repeated across many steps can become a noticeable delay for the user.

One on-device baseline from our Mano-CUA testing

In one existing test, we ran Mano-CUA-Thinking-4B on a MacBook Pro M5 with 16GB of memory and evaluated it on 100 real macOS GUI tasks.

The recorded results were:

• Task pass rate: 56%
• Average inference time per step: 7.9 seconds
• Average number of steps per task: 11.5 This is not a universal benchmark for every Mac or every GUI agent. It is a baseline from a specific device, model, and task set.

What it illustrates is that task-level evaluation matters. A model starting successfully does not mean the complete interaction loop is usable.

Desktop, laptop, or dedicated hardware?

We think these form factors should be evaluated against their intended use cases rather than ranked with one universal score.

Desktop machines can make sense for fixed workstations or workloads that need to run for long periods. Power and maintenance may be easier to manage, but display sessions, external peripherals, sleep settings, and remote administration still matter.

Laptops offer an integrated screen, keyboard, trackpad, and compute unit. They are convenient for individual use and development. Battery state, power profiles, and sustained thermal behavior can affect long-running workloads.

Dedicated devices may be useful when an agent needs to run as part of a fixed workflow. But a small form factor is not automatically an advantage. We still need to evaluate compute capacity, I/O, system updates, recovery, and the effort required to maintain the device.

For GUI agents, there is another practical detail: the desktop session itself matters. A locked screen, a sleeping machine, a changed display resolution, or an unavailable input device can interrupt the workflow even when model inference is working correctly.

What should a fair cross-device test include?

If we compare the same agent across a desktop, a laptop, and a dedicated device, we should keep the variables as consistent as possible:

1. Use the same model, runtime, and quantization configuration.
2. Run the same task set from the same initial UI state.
3. Define task success before running the test.
4. Repeat tasks and report both averages and failure cases.
5. Measure per-step latency and total task time.
6. Record memory pressure, power state, and sustained performance.
7. Separate model failures from UI execution and recovery failures. Without these controls, a device comparison can easily become a comparison of different software configurations or different tasks.

We also have separate inference measurements on M5 Pro. For a 4B vision-language model with a 4,516-token context, the W8A16 baseline recorded 2.839 seconds of prefill and 80.1 tokens/s decode. With Cider W8A8, prefill was 2.519 seconds and decode was 79.5 tokens/s.

Those measurements are useful for understanding runtime behavior, but they were collected under a different setup from the 100-task GUI evaluation. We should not combine them into one cross-device benchmark.

The question is not simply “Which device is fastest?”

For an on-device agent, the better question is: which hardware form factor can complete the target workflow reliably, at an acceptable interaction speed, with manageable deployment and maintenance costs?

That answer may be different for a developer's laptop, a fixed office workstation, and a dedicated device in a specific environment.

If you are running GUI agents locally, what do you optimize for first: task success rate, end-to-end latency, sustained performance, or ease of deployment? And what hardware are you using?