GLM-4 9B needs ~7.8 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~22 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 with offload
Decode
21.9 tok/s
TTFT
8854 ms
Safe context
21K
Memory
7.8 GB / 8.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 21.9 tok/s | 4829 ms | 21K |
| Coding | A | Runs with offload | 21.9 tok/s | 8854 ms | 21K |
| Agentic Coding | B | Runs with offload (needs ~0.3 GB host RAM) | 14.8 tok/s | 19079 ms | 21K |
| Reasoning | A | Runs with offload | 21.9 tok/s | 10463 ms | 21K |
| RAG | B | Runs with offload (needs ~0.3 GB host RAM) | 14.8 tok/s | 23848 ms | 21K |
How GLM-4 9B (9B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | A75 |
Q3_K_S | 3 | 4.4 GB | Low | A75 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | A74 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
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 glm4Yes, Intel Arc A550M 8GB can run GLM-4 9B with a A grade (Runs with offload). Expected decode speed: 21.9 tok/s.
GLM-4 9B (9B parameters) requires approximately 7.8 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 A550M 8GB, GLM-4 9B achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8854ms using Q4_K_M quantization.
For coding workloads, GLM-4 9B on Intel Arc A550M 8GB receives a A grade with 21.9 tok/s and 21K context.
On Intel Arc A550M 8GB, GLM-4 9B can safely use up to 21K 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.
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