GLM-4 9B needs ~8.9 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~22 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
21.8 tok/s
TTFT
8875 ms
Safe context
121K
Memory
8.9 GB / 13.0 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 21.8 tok/s | 4841 ms | 121K |
| Coding | A | Runs well | 21.8 tok/s | 8875 ms | 121K |
| Agentic Coding | A | Runs well | 21.8 tok/s | 12908 ms | 121K |
| Reasoning | A | Runs well | 21.8 tok/s | 10488 ms | 121K |
| RAG | A | Runs well | 21.8 tok/s | 16136 ms | 121K |
How GLM-4 9B (9B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A71 |
Q3_K_S | 3 | 4.4 GB | Low | A72 |
NVFP4 | 4 |
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3 14B | 14B | A | 12.3 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | A | 10.6 tok/s |
Yes, MacBook Pro M3 Pro 18GB can run GLM-4 9B with a A grade (Runs well). Expected decode speed: 21.8 tok/s.
GLM-4 9B (9B parameters) requires approximately 8.9 GB of memory with Q4_K_M quantization.
The recommended quantization for GLM-4 9B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 18GB, GLM-4 9B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8875ms using Q4_K_M quantization.
For coding workloads, GLM-4 9B on MacBook Pro M3 Pro 18GB receives a A grade with 21.8 tok/s and 121K context.
On MacBook Pro M3 Pro 18GB, GLM-4 9B can safely use up to 121K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/glm-4-9b-on-m3-pro-18gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
5.0 GB |
| Medium |
| A72 |
Q4_K_M | 4 | 5.5 GB | Medium | A73 |
Q5_K_M | 5 | 6.5 GB | High | A73 |
Q6_K | 6 | 7.4 GB | High | A73 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | A73 |
F16 | 16 | 18.5 GB | Maximum | F0 |
 Ministral 3 14B | 14B | A | 12.3 tok/s |
| Phi-4 14B | 14B | B | 11.6 tok/s |
| Qwen 2.5 14B | 14B | B | 11.7 tok/s |
Not always. MacBook Pro M3 Pro 18GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.