GoKawiilApple Silicon and macOS VMs: 11–16× Faster LLM Inference with llama.cpp
Published on August 11, 2026 by Francesco Bonacci and Johnny Franks
If you've been following Cua from the start, you may remember that it began with a Show HN launch for Lume, our macOS virtualization stack.
Today, we're sharing the first result from a broader effort to connect that Virtualization.framework foundation to the local computer-use environments behind Cua Driver and the infrastructure behind Cua Cloud and Fleets: a small, process-scoped compatibility layer that unlocks newer Metal fast paths inside a macOS guest.
We're releasing this work today as a research release under the same permissive license as Lume and Cua, so others can reproduce the results and help map which Apple Silicon chips, macOS releases, and Metal workloads benefit.
Apple Vz users have been running into these limitations elsewhere too. Tart, another notable CLI built on Apple's Virtualization.framework , has an open “No GPU passthrough in macOS guest?” issue asking whether the framework can provide usable graphics and decent LLM performance in a macOS VM guest. The VM continues to use the virtual GPU that Apple provides. Our work exposes newer Metal paths on that device and closes part of the practical gap.
On an M1 Ultra, TinyLlama 1.1B running through llama.cpp processed prompts 11.08× faster and generated tokens 16.36× faster than the same workload in the same stock VM. Prompt processing reached 98% of our bare-metal result. The source, build scripts, capability probe, and raw benchmark logs are included so you can inspect and reproduce the result.
We repeated the experiment with Google's Gemma 4 12B QAT Q4_0, a 6.98 GB model released this year. The same layer improved prompt processing 7.20× and token generation 14.54×. The unlocked VM reached 99.59% of bare-metal prompt speed and 94.82% of bare-metal generation speed.
The cap inside a macOS VM
Apple's Virtualization.framework presents a macOS guest with a virtual graphics device. The guest submits Metal work through a purpose-built GPU driver, and Apple's host stack executes it on the physical GPU. This arrangement is paravirtualization, where the host keeps control of the hardware and the guest uses a virtualization-aware device.
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