Cua Speeds LLM Inference 16x in macOS VMs on Apple Silicon
TL;DR. Cua's new compatibility layer significantly accelerates LLM inference in macOS virtual machines on Apple Silicon chips. - The layer closes a practical performance gap by exposing newer Metal fast paths within a macOS guest VM. - Benchmarks show TinyLlama 1.1B inference up to 16.36x faster on an M1 Ultra, nearly matching bare-metal speeds. - The technology also improved Google's Gemma 12B QAT performance by up to 14.54x in a VM environment.
- Cua released a compatibility layer to improve Metal performance for LLMs in macOS VMs.
- The layer unlocks newer Metal fast paths, addressing previous limitations in Apple's Virtualization.framework.
- TinyLlama 1.1B inference was up to 16.36x faster in a VM on M1 Ultra, reaching 98% of bare-metal prompt processing speed.
- Google's Gemma 12B QAT also saw significant gains, with token generation 14.54x faster and prompt speed at 99.59% of bare-metal.
- The research release includes source code, build scripts, and raw benchmark logs for reproduction and further development.