Can I Run GLM-5 on a Apple M3 Ultra (512GB)?
Yes
Runs at full precision (fp16). Zero quality loss.
Model size
230B
GPU memory
512GB
Smallest quant
Q4_K_M
Best fit
fp16
5 quantizations fit your 512GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| fp16BEST | 461.0 GB | 462.5 GB | 460.0 GB | +51.0 GB |
| Q8_0 | 245.4 GB | 246.9 GB | 244.4 GB | +266.6 GB |
| Q6_K | 190.5 GB | 192.0 GB | 189.5 GB | +321.5 GB |
| Q5_K_M | 164.3 GB | 165.8 GB | 163.3 GB | +347.7 GB |
| Q4_K_M | 140.4 GB | 141.9 GB | 139.4 GB | +371.6 GB |
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Full model details
GLM-5 →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
Apple M3 Ultra (512GB) →
Top-ranked open-source models that fit in 512GB.
FAQ
Can the Apple M3 Ultra (512GB) run GLM-5?
Yes. The Apple M3 Ultra (512GB)'s 512GB of unified memory is enough to run GLM-5 at fp16 quantization (461.0GB required).
What's the best quantization to use?
fp16 is the highest-precision quantization that fits in your 512GB. It uses about 461.0GB of memory and 462.5GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.