Raises estimated decode speed by about 258%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
GLM-4 9B needs ~9.6 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~12 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
12.9 tok/s
TTFT
14950 ms
Safe context
128K
Memory
9.6 GB / 17.3 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 | B | Runs well | 11.8 tok/s | 8919 ms | 128K |
| Coding | B | Runs well | 11.8 tok/s | 16352 ms | 128K |
| Agentic Coding | B | Runs well | 11.8 tok/s | 23784 ms | 128K |
| Reasoning | B | Runs well | 11.8 tok/s | 19325 ms | 128K |
| RAG | B | Runs well | 11.8 tok/s | 29730 ms | 128K |
How GLM-4 9B (9B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B68 |
Q3_K_S | 3 | 4.4 GB | Low | B69 |
NVFP4 | 4 |
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4Upgrade options
Raises estimated decode speed by about 258%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 116%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 616%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M2 24GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 11.8 tok/s.
GLM-4 9B (9B parameters) requires approximately 9.6 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 Mac mini M2 24GB, GLM-4 9B achieves approximately 11.8 tokens per second decode speed with a time-to-first-token of 16352ms using Q4_K_M quantization.
For coding workloads, GLM-4 9B on Mac mini M2 24GB receives a B grade with 11.8 tok/s and 128K context.
On Mac mini M2 24GB, GLM-4 9B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Not always. Mac mini M2 24GB 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/glm-4-9b-on-m2-24gb" 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 |
| B69 |
Q4_K_M | 4 | 5.5 GB | Medium | B70 |
Q5_K_M | 5 | 6.5 GB | High | A71 |
Q6_K | 6 | 7.4 GB | High | A72 |
Q8_0Best for your GPU | 8 | 9.6 GB | Very High | A73 |
F16 | 16 | 18.5 GB | Maximum | F0 |