GLM-4 9B needs ~8.5 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~63 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
62.8 tok/s
TTFT
3081 ms
Safe context
108K
Memory
8.5 GB / 12.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 62.8 tok/s | 1681 ms | 108K |
| Coding | A | Runs well | 62.8 tok/s | 3081 ms | 108K |
| Agentic Coding | A | Runs well | 62.8 tok/s | 4482 ms | 108K |
| Reasoning | A | Runs well | 62.8 tok/s | 3642 ms | 108K |
| RAG | A | Runs well | 62.8 tok/s | 5603 ms | 108K |
How GLM-4 9B (9B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A71 |
Q3_K_S | 3 | 4.4 GB | Low | A73 |
NVFP4 | 4 | 5.0 GB | Medium | A73 |
Q4_K_M | 4 | 5.5 GB | Medium | A74 |
Q5_K_M | 5 | 6.5 GB | High | A74 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | A73 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
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 | 23.8 tok/s | |
 Ministral 3 14B | 14B | A | 23.7 tok/s | |
 Phi-4 14B | 14B | A | 21.5 tok/s | |
| Qwen 2.5 14B | 14B | A | 22.1 tok/s | |
 OLMo 2 13B | 13B | B | 28.3 tok/s |
Yes, RTX 4000 Ada Laptop 12GB can run GLM-4 9B with a A grade (Runs well). Expected decode speed: 62.8 tok/s.
GLM-4 9B (9B parameters) requires approximately 8.5 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 RTX 4000 Ada Laptop 12GB, GLM-4 9B achieves approximately 62.8 tokens per second decode speed with a time-to-first-token of 3081ms using Q4_K_M quantization.
For coding workloads, GLM-4 9B on RTX 4000 Ada Laptop 12GB receives a A grade with 62.8 tok/s and 108K context.
On RTX 4000 Ada Laptop 12GB, GLM-4 9B can safely use up to 108K 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-rtx-4000-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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