Qwen 3 4B needs ~6.1 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~30 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
100 MB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
29.6 tok/s
TTFT
6531 ms
Safe context
15K
Memory
6.1 GB / 6.0 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 | 41.4 tok/s | 2548 ms | 15K |
| Coding | A | Runs with offload (needs ~0.1 GB host RAM) | 29.6 tok/s | 6531 ms | 15K |
| Agentic Coding | F | Too heavy | 15.6 tok/s | 18093 ms | 15K |
| Reasoning | A | Runs with offload (needs ~0.1 GB host RAM) | 29.6 tok/s | 7719 ms | 15K |
| RAG | F | Too heavy | 15.6 tok/s | 22616 ms |
How Qwen 3 4B (4B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | S86 |
Q3_K_S | 3 | 2.0 GB | Low | S86 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3 4B on your machine.
Run
ollama run qwen3:4bYes, Intel Arc Pro A40 6GB can run Qwen 3 4B with a A grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 29.6 tok/s.
Qwen 3 4B (4B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 4B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A40 6GB, Qwen 3 4B achieves approximately 29.6 tokens per second decode speed with a time-to-first-token of 6531ms using Q4_K_M quantization.
For coding workloads, Qwen 3 4B on Intel Arc Pro A40 6GB receives a A grade with 29.6 tok/s and 15K context.
On Intel Arc Pro A40 6GB, Qwen 3 4B can safely use up to 15K tokens of context. The model's official context limit is 33K, 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/qwen-3-4b-on-arc-pro-a40-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 15K |
2.2 GB |
| Medium |
| S86 |
Q4_K_M | 4 | 2.4 GB | Medium | S86 |
Q5_K_M | 5 | 2.9 GB | High | S85 |
Q6_KBest for your GPU | 6 | 3.3 GB | High | S85 |
Q8_0 | 8 | 4.3 GB | Very High | F0 |
F16 | 16 | 8.2 GB | Maximum | F0 |
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.