GLM-4 9B needs ~9.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 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
49.1 tok/s
TTFT
3946 ms
Safe context
128K
Memory
9.4 GB / 24.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 49.1 tok/s | 2153 ms | 128K |
| Coding | B | Runs well | 49.1 tok/s | 3946 ms | 128K |
| Agentic Coding | A | Runs well | 49.1 tok/s | 5740 ms | 128K |
| Reasoning | B | Runs well | 49.1 tok/s | 4664 ms | 128K |
| RAG | A | Runs well | 49.1 tok/s | 7175 ms | 128K |
How GLM-4 9B (9B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | B66 |
Q3_K_S | 3 | 4.4 GB | Low | B67 |
NVFP4 | 4 | 5.0 GB | Medium | B67 |
Q4_K_M | 4 | 5.5 GB | Medium | B67 |
Q5_K_M | 5 | 6.5 GB | High | B68 |
Q6_K | 6 | 7.4 GB | High | B68 |
Q8_0 | 8 | 9.6 GB | Very High | B70 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | A71 |
Copy-paste commands to run GLM-4 9B on your machine.
Run
ollama run glm4Upgrade options
RTX 5090 32GBBudget pickRaises estimated decode speed by about 248%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$1,999 MSRP
~$2,499 MSRP
Yes, Intel Arc Pro B60 24GB can run GLM-4 9B with a B grade (Runs well). Expected decode speed: 49.1 tok/s.
GLM-4 9B (9B parameters) requires approximately 9.4 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 Intel Arc Pro B60 24GB, GLM-4 9B achieves approximately 49.1 tokens per second decode speed with a time-to-first-token of 3946ms using Q4_K_M quantization.
For coding workloads, GLM-4 9B on Intel Arc Pro B60 24GB receives a B grade with 49.1 tok/s and 128K context.
On Intel Arc Pro B60 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.
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/glm-4-9b-on-arc-pro-b60-24gb" 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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