GLM-4 9B needs ~10.5 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~120 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
120.1 tok/s
TTFT
1612 ms
Safe context
128K
Memory
10.5 GB / 32.0 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 | 120.1 tok/s | 879 ms | 128K |
| Coding | A | Runs well | 120.1 tok/s | 1612 ms | 128K |
| Agentic Coding | A | Runs well | 120.1 tok/s | 2344 ms | 128K |
| Reasoning | A | Runs well | 120.1 tok/s | 1905 ms | 128K |
| RAG | A | Runs well | 120.1 tok/s | 2930 ms | 128K |
How GLM-4 9B (9B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B65 |
Q3_K_S | 3 | 4.4 GB | Low | B65 |
NVFP4 | 4 | 5.0 GB | Medium | B65 |
Q4_K_M | 4 | 5.5 GB | Medium | B65 |
Q5_K_M | 5 | 6.5 GB | High | B66 |
Q6_K | 6 | 7.4 GB | High | B66 |
Q8_0 | 8 | 9.6 GB | Very High | B67 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | A71 |
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 91.2 tok/s | |
| Qwen 3.5 27B | 27B | S | 39.5 tok/s | |
| Qwen 3.6 27B | 27B | S | 39.7 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 76.6 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 94.3 tok/s |
Yes, NVIDIA V100 32GB can run GLM-4 9B with a A grade (Runs well). Expected decode speed: 120.1 tok/s.
GLM-4 9B (9B parameters) requires approximately 10.5 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 NVIDIA V100 32GB, GLM-4 9B achieves approximately 120.1 tokens per second decode speed with a time-to-first-token of 1612ms using Q4_K_M quantization.
For coding workloads, GLM-4 9B on NVIDIA V100 32GB receives a A grade with 120.1 tok/s and 128K context.
On NVIDIA V100 32GB, 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-v100-32gb" 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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