Qwen 3.5 0.6B needs ~2.5 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~8 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
8.4 tok/s
TTFT
23048 ms
Safe context
44K
Memory
2.5 GB / 4.0 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 | C | Runs well | 8.4 tok/s | 12571 ms | 44K |
| Coding | C | Runs well | 8.4 tok/s | 23048 ms | 44K |
| Agentic Coding | C | Tight fit | 8.4 tok/s | 33524 ms | 44K |
| Reasoning | C | Runs well | 8.4 tok/s | 27238 ms | 44K |
| RAG | C | Tight fit | 8.4 tok/s | 41905 ms | 44K |
How Qwen 3.5 0.6B (0.6000000238418579B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.2 GB | Low | B58 |
Q3_K_S | 3 | 0.3 GB | Low | B59 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3.5 0.6B on your machine.
Run
ollama run qwen3.5:0.6bYes, Intel Arc A370M 4GB can run Qwen 3.5 0.6B with a C grade (Runs well). Expected decode speed: 8.4 tok/s.
Qwen 3.5 0.6B (0.6000000238418579B parameters) requires approximately 2.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3.5 0.6B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, Qwen 3.5 0.6B achieves approximately 8.4 tokens per second decode speed with a time-to-first-token of 23048ms using Q4_K_M quantization.
For coding workloads, Qwen 3.5 0.6B on Intel Arc A370M 4GB receives a C grade with 8.4 tok/s and 44K context.
On Intel Arc A370M 4GB, Qwen 3.5 0.6B can safely use up to 44K tokens of context. The model's official context limit is 131K, 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-3.5-0.6b-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| B59 |
Q4_K_M | 4 | 0.4 GB | Medium | B59 |
Q5_K_M | 5 | 0.4 GB | High | B59 |
Q6_K | 6 | 0.5 GB | High | B60 |
Q8_0 | 8 | 0.6 GB | Very High | B60 |
F16Best for your GPU | 16 | 1.2 GB | Maximum | B59 |
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.