Mac Studio Users Struggle with AI Video Generation via ComfyUI and Wan2.2 5B

Despite possessing one of the most powerful consumer-grade machines on the market—the Mac Studio with M3 Ultra’s 32-core CPU, 80-core GPU, and 256GB of RAM—users are encountering significant hurdles in deploying advanced AI video generation tools. A recent Reddit thread from a user known as /u/ridlerontheroof highlights growing frustration among Apple Silicon adopters trying to run Wan2.2 5B ti2v (text-to-video) via ComfyUI, an open-source graphical interface for Stable Diffusion workflows. The user reports that even after extensive tuning, generating a three-second video clip can take over an hour, with outputs frequently ignoring prompt instructions and exhibiting low fidelity.

The issue underscores a broader challenge in the generative AI community: while hardware capabilities have surged, software optimization for Apple’s ARM-based architecture lags behind. Unlike NVIDIA’s CUDA-optimized ecosystems, Apple Silicon relies on Metal Performance Shaders (MPS) for GPU acceleration, which many AI frameworks, including key components of ComfyUI, support only partially or experimentally. As a result, workflows designed for x86 systems often fail to translate efficiently to macOS, leading to degraded performance, memory leaks, and inconsistent results.

According to user reports and community discussions, the scarcity of compatible node templates for Wan2.2 5B ti2v exacerbates the problem. Many publicly shared workflows are built for NVIDIA-based Linux or Windows systems and lack the necessary adaptations for MPS backend integration. Users attempting to adapt these workflows often encounter missing dependencies, unsupported operations, or unoptimized tensor handling, which further slow down inference. Even when a workflow runs, the lack of real-time feedback and debugging tools in ComfyUI on macOS makes iterative refinement a painstaking process.

While Apple’s hardware boasts impressive theoretical performance, the software stack for AI inference remains fragmented. Unlike Google’s ecosystem, which provides robust support for productivity tools like Google Keep—evidenced by detailed documentation on creating, editing, and sharing notes across platforms—the AI generative space lacks equivalent developer resources for macOS users. Google Keep’s help center, for instance, offers step-by-step guides for users on desktop, Android, and iOS, ensuring broad accessibility. In contrast, AI video generation tools on Apple Silicon operate in a patchwork of community-driven fixes, with no official support or optimized distributions from model developers.

Some community members have suggested workarounds, including downgrading to Wan2.1 models with better MPS compatibility, using external cloud rendering services, or employing conversion tools to port models to Core ML format. However, these solutions require advanced technical knowledge and often sacrifice quality or functionality. A few developers have begun experimenting with PyTorch 2.3’s enhanced MPS backend, but widespread adoption is still months away.

For now, Mac Studio users seeking reliable AI video generation face a choice: compromise on output quality, invest in cloud-based alternatives, or wait for the open-source community to bridge the gap. The situation highlights a critical need for greater collaboration between AI model creators, framework developers, and hardware vendors to ensure equitable performance across platforms. Without standardized, optimized tooling for Apple Silicon, the promise of desktop AI creativity may remain out of reach for a significant segment of users—even those with top-tier hardware.

As the generative AI race accelerates, the disparity between hardware potential and software maturity on macOS serves as a cautionary tale. What should be a seamless creative workflow is instead a labyrinth of trial, error, and undocumented workarounds—reminding us that raw processing power alone cannot deliver innovation without thoughtful, platform-aware engineering.