Exp-Pi

Background Since the introduction of the Apple Silicon chip series, Apple has consistently highlighted its exceptional capabilities in image processing and AI computation. The unified memory architecture provides significantly higher memory bandwidth, enabling accelerated performance for AI model workloads. Within the community, while there is extensive discussion around models, workflows, and quantization techniques for acceleration, there is relatively little detailed data or analysis regarding their performance on Mac systems. Some users are curious about how the MacBook Pro compares to systems equipped with NVIDIA RTX discrete GPUs. They seek a balance between the portability and productivity benefits of macOS and the ability to engage in AI-related development and design tasks. Content This analysis evaluates the performance of several mainstream image generation models on an Apple Silicon MacBook Pro equipped with the M4 Max chip and 128 GB of unified memory. The selected models ...

Fine-tuning large models is typically done on CUDA-enabled devices. Whether using consumer-grade GPUs or specialized AI accelerator cards, the cost is often high. These setups also demand substantial power and efficient cooling, which usually requires a large desktop workstation. Alternatively, you can rent cloud computing resources by the hour using platforms like Runpod or Lambda.ai. However, this still incurs significant costs and often requires considerable time to upload data from your local machine to the cloud. Since Apple introduced its Silicon chip series, PyTorch has added support for the MPS (Metal Performance Shaders) backend on M1 and later devices, significantly improving compute performance on macOS. Thanks to the unified memory architecture of Apple Silicon, it’s possible to load larger models than what most consumer GPUs can handle, reducing the constraints imposed by limited VRAM. This allows developers to fine-tune models locally while still enjoying the portability...