text model · GLM · macOS
Can I run GLM-4-32B-0414 on Apple M5 Pro (48GB)?
Yes. GLM-4-32B-0414 runs on Apple M5 Pro (48GB) at Q4_K_M (~20.5 GB of ~32 GB usable).
Runs at Q4_K_M using ~20.5 GB of ~32 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 Pro (48GB) leaves ~11.5 GB of headroom.
- Q4_K_M needed
- ~20.5 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf bartowski/THUDM_GLM-4-32B-0414-GGUF:Q4_K_M lms get bartowski/THUDM_GLM-4-32B-0414-GGUF How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 32B
- Q4_K_M size
- 18.33 GB
- Q8_0 size
- 32.24 GB
- Context
- 32k
- Memory
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run GLM-4-32B-0414 on other hardware
FAQ
Can Apple M5 Pro (48GB) run GLM-4-32B-0414?
Yes. GLM-4-32B-0414 runs on Apple M5 Pro (48GB) at Q4_K_M (~20.5 GB of ~32 GB usable).
How much memory does GLM-4-32B-0414 need?
Apple M5 Pro (48GB) has room to spare. At Q4_K_M the weights are ~18.33 GB; with KV cache and runtime overhead, budget ~20.5 GB at a 4k context.
What is the best tool to run GLM-4-32B-0414 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-4-32b-0414/apple-m5-pro-48gb) Sources
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.