Unsloth squeezes a 753B AI model to run locally on a Mac

What if you could run a 753-billion-parameter AI model on a single Mac? That's exactly what Unsloth AI has pulled off. The company shrunk Zhipu AI's open-source GLM-5.2 model by over 80% using dynamic 1-bit and 2-bit quantization, bringing its weight from a colossal 1.51 TB down to just 217 GB (1-bit variant) or 239 GB (2-bit UD-IQ2_M). That means developers and small businesses can now deploy the model offline on a single Mac Studio, no expensive multi-GPU clusters required.

The compressed GGUF version runs at 21.6 tokens per second on a Mac Studio M3 Ultra with 256 GB of unified memory, while retaining 76% to 82% of the original model's accuracy. In Unsloth's benchmark, the 1-bit GLM-5.2 GGUF held its own against Claude 4.8 Opus and GPT-5.5 when tasked with generating a complete HTML5 game — a Flappy Bird clone called Sunset Flier with pixel art, sound effects, and a particle system.

GLM-5.2 is a mixture-of-experts (MoE) model with 753B total parameters and a 1-million-token context window. By slashing its memory footprint, Unsloth's dynamic quantization breaks the hardware barrier that previously forced users to rely on costly cloud infrastructure. The GLM-5.2 GGUF weights are now available on Hugging Face, and you can load them directly with llama.cpp or Unsloth Studio.