Running GLM-5.2 on a 64GB Mac, barely
The author attempted to run GLM-5.2, a large language model, on a 64GB Mac, and found that it barely worked, with the system struggling under memory constraints and performance issues during inference.
Background
- GLM-5.2 is a large language model (LLM) developed by Zhipu AI (智谱AI), a leading Chinese AI company. It is one of the most capable open-weight models to come out of China, comparable to GPT-4 or Llama-3 in scale.
- Running a model of this size (likely hundreds of billions of parameters) on a single Mac with 64GB of RAM is unusual. Normally, such models require datacenter GPUs with high VRAM. The author is likely using quantization (reducing numerical precision to shrink memory footprint) and offloading layers to unified memory, which is possible on Apple Silicon because the CPU and GPU share the same memory pool.
- "Barely" hints at extreme memory pressure — the system is probably using swap, running at the edge of stability, and getting very slow token generation (low tokens/second). This is a hacker/enthusiast experiment, not a production deployment.
- The post implicitly showcases how far consumer hardware can go with modern LLMs and quantization techniques, and it also reflects the growing availability of competitive Chinese models outside the usual Western ecosystem (OpenAI, Meta, Google).