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Virtualization on macOS has become rock-solid in the Apple Silicon era. Parallels Desktop 27 pushes that further with a new Metal-based graphics driver, OpenGL 4.3 support for Windows virtual machines on Apple silicon, faster local AI workloads, and a host of enterprise features aimed at IT teams, helping them manage and deploy Windows on macOS.
About Apple @ Work: Bradley Chambers has been an Apple IT admin since 2009. Through his experience deploying and managing firewalls, switches, a mobile device management system, enterprise grade WiFi, 1000s of Macs, and 1000s of iPads, Bradley will highlight ways in which Apple IT managers deploy Apple devices, build networks to support them, train users, share stories from the trenches of IT management, and ways Apple could improve its products for IT departments.
Graphic performance improvements
The headline feature of Parallels Desktop 27 is a new Metal-based graphics driver that delivers hardware-accelerated OpenGL 4.3 support to Windows virtual machines on Apple silicon, something Parallels says is unique among Mac virtualization tools right now. OpenGL 4.3 is the minimum graphics level required by many professional Windows apps. ArcGIS Pro, ZWCAD, and Blender 4.3 can now run in a virtualized environment on macOS thanks to this enhancement.
Parallels claims up to 160% faster OpenGL graphics performance on M3 and newer Macs. While boasting up to 35% faster 3D rendering in ArcGIS Pro, which cuts overall workflow time by up to 24%.
Better OpenGL support doesn’t just help professional engineering software. It helps anything that leans on that graphics stack, including games. I’m gaming on a PC for the first time in 20 years right now, all of it in a Windows VM on my M5 Pro, and it’s been incredible as I’ve been testing Parallels Desktop 27.
Local AI development gets faster
Parallels Desktop 27 also adds support for Scalable Matrix Extension (SME2) instructions in Linux and Windows VMs on M4 and newer Macs. Parallels notes that this translates to up to 7x faster AI and machine learning matrix operations and up to 1.75x faster neural network inference, with better performance for tools like Ollama and llama.cpp running inside a VM. For developers who want to build and test AI workloads locally rather than using cloud credits for every prompt, that’s a nice upgrade and further highlights the importance of Apple Silicon in the AI era.
What’s new for IT?
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