text model · GLM · macOS
Can I run GLM-5.2 on Apple M4 Pro (24GB)?
No. GLM-5.2 needs ~473.1 GB even at Q4_K_M, but Apple M4 Pro (24GB) only has ~16 GB usable.
Needs ~473.1 GB even at Q4_K_M, but only ~16 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 457.1 GB: GLM-5.2 needs roughly 473.1 GB at Q4_K_M and Apple M4 Pro (24GB) leaves only about 16 GB usable for a model. No single tracked device has enough memory; GLM-5.2 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~473.1 GB
- Usable on device
- ~16 GB
- Device memory
- 24 GB
Which quant fits
How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 744B (MoE, 40B active)
- Q4_K_M size
- 465.83 GB
- Q8_0 size
- 801.36 GB
- Context
- 1000k
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Power draw
- ~140 W
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
What you can run instead
FAQ
Can Apple M4 Pro (24GB) run GLM-5.2?
No. GLM-5.2 needs ~473.1 GB even at Q4_K_M, but Apple M4 Pro (24GB) only has ~16 GB usable.
How much memory does GLM-5.2 need?
Apple M4 Pro (24GB) does not have enough memory. At Q4_K_M the weights are ~465.83 GB; with KV cache and runtime overhead, budget ~473.1 GB at a 4k context. It is a Mixture-of-Experts model (744B total / 40B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run GLM-5.2 on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-5.2/apple-m4-pro-24gb) Sources
- apple.com/newsroom/2024/10/apple-introduces-m4-pro-and-m4-max
- apple.com/newsroom/2024/10/new-macbook-pro-features-m4-family-of-chips-and-apple-intelligence
- developer.apple.com
- github.com/ml-explore
- github.com/raullenchai
- github.com/zai-org
- huggingface.co/unsloth
- huggingface.co/zai-org
- lmstudio.ai
- support.apple.com/en-us/103253
- support.apple.com/en-us/121553
- support.apple.com/en-us/121555
- z.ai
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.