GLM-4 9B needs ~8.9 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 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
31.1 tok/s
TTFT
6229 ms
Safe context
128K
Memory
8.9 GB / 16.0 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 | 31.1 tok/s | 3398 ms | 128K |
| Coding | A | Runs well | 31.1 tok/s | 6229 ms | 128K |
| Agentic Coding | A | Runs well | 31.1 tok/s | 9061 ms | 128K |
| Reasoning | A | Runs well | 31.1 tok/s | 7362 ms | 128K |
| RAG | A | Runs well | 31.1 tok/s | 11326 ms | 128K |
How GLM-4 9B (9B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B69 |
Q3_K_S | 3 | 4.4 GB | Low | B70 |
NVFP4 | 4 | 5.0 GB | Medium | A70 |
Q4_K_M | 4 | 5.5 GB | Medium | A71 |
Q5_K_M | 5 | 6.5 GB | High | A72 |
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 |
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 | S | 19.7 tok/s | |
 Phi-4-reasoning-plus 14B | 14.7B | S | 18.7 tok/s | |
 GPT-OSS 20B | 21B | A | 17.4 tok/s | |
 Ministral 3 14B | 14B | A | 19.6 tok/s | |
| Codestral 2 25.08 | 22B | B | 6.8 tok/s |
Yes, NVIDIA A2 16GB can run GLM-4 9B with a A grade (Runs well). Expected decode speed: 31.1 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 NVIDIA A2 16GB, GLM-4 9B achieves approximately 31.1 tokens per second decode speed with a time-to-first-token of 6229ms using Q4_K_M quantization.
For coding workloads, GLM-4 9B on NVIDIA A2 16GB receives a A grade with 31.1 tok/s and 128K context.
On NVIDIA A2 16GB, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/glm-4-9b-on-a2-16gb" 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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