text model · GLM · macOS
Can I run GLM-4-9B-0414 on Apple M1 (8GB)?
No. GLM-4-9B-0414 needs ~7.2 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~7.2 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 1.7 GB: GLM-4-9B-0414 needs roughly 7.2 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 GB usable for a model. The lightest tracked hardware that runs GLM-4-9B-0414 is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See GLM-4-9B-0414 on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~7.2 GB
- Usable on device
- ~5.5 GB
- Device memory
- 8 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
- 9B
- Q4_K_M size
- 5.74 GB
- Q8_0 size
- 9.31 GB
- Context
- 32k
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
Run GLM-4-9B-0414 on other hardware
FAQ
Can Apple M1 (8GB) run GLM-4-9B-0414?
No. GLM-4-9B-0414 needs ~7.2 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does GLM-4-9B-0414 need?
Apple M1 (8GB) does not have enough memory. At Q4_K_M the weights are ~5.74 GB; with KV cache and runtime overhead, budget ~7.2 GB at a 4k context.
What is the best tool to run GLM-4-9B-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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-4-9b-0414/apple-m1-8gb) 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.