When judging the efficiency of Apple Silicon or any other platform, one might be tempted to just glance at a power meter like a Kill-a-Watt with the system under load and leave it at that, but instantaneous power draw isn’t the right metric when judging the efficiency of a system for a given task.
Task energy (the amount of power consumed over time) is much more informative. A system that needs a lot of power but finishes a task quickly may consume less task energy and is therefore more efficient than one that consumes a moderate amount of power but takes much longer to finish a job.
To judge inference efficiency, we set up a fixed-length workload with gpt-oss-120b using llama.cpp and llama-benchy: first processing a 2048-token prompt against a 32768-token context depth, then generating 1024 output tokens. We chose that context depth to ensure that our test ran long enough to collect useful data. We recorded this data in watt-hours so that it reflects both energy consumed and time to completion.
At least in this synthetic workload, the M4 Max is more efficient than Strix Halo, but the sheer prompt-processing speed of GB10 lets it finish much faster and use less total power than the Mac Studio does, even though it’s outputting fewer tokens per second in the decode stage.
This test once again suggests that fixating on tokens-per-second throughput alone is perhaps not ideal for judging a system’s inference performance. An ideal LLM inference system would have both high prompt processing speeds and high tokens-per-second throughput for the shortest possible inference turn.
We’re curious how Apple’s latest M5 Max GPU would perform on this workload, as the company claims big gains on prefill compared to the M4 Max and has given that chip even higher memory bandwidth than the M4 Max. If that’s true, the combination of fast prefill and high tokens-per-second on decode would likely make the M5 Max competitive with GB10, if not an outright leader here.
We also gauged idle and load noise levels of these systems to see what it’s like to share space with them. For reference, the noise floor in our testing environment is about 32.6 dBA. We measure sound pressure levels at a distance of 18” from these systems to approximate what it’s like to work with them on top of a desk.
The Mac Studio is impressively quiet both at idle and under load. It barely raises the noise level in our testing environment at idle, and under load, its fans are barely perceptible. Practically any other noise you make or hear in your space will be louder. This is superlative performance.
