Qwen 3 4B needs ~6.5 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~56 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
56.0 tok/s
TTFT
3457 ms
Safe context
33K
Memory
6.5 GB / 10.0 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 | A | Runs well | 56.0 tok/s | 1886 ms | 33K |
| Coding | S | Runs well | 56.0 tok/s | 3457 ms | 33K |
| Agentic Coding | A | Tight fit | 56.0 tok/s | 5029 ms | 33K |
| Reasoning | S | Runs well | 56.0 tok/s | 4086 ms | 33K |
| RAG | A | Tight fit | 56.0 tok/s | 6286 ms | 33K |
How Qwen 3 4B (4B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.6 GB | Low | A80 |
Q3_K_S | 3 | 2.0 GB | Low | A81 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3 4B on your machine.
Run
ollama run qwen3:4bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen 3.5 9B | 9B | S | 40.2 tok/s | |
| Qwen 3 8B | 8B | S | 45.2 tok/s |
Yes, Intel Arc B570 10GB can run Qwen 3 4B with a S grade (Runs well). Expected decode speed: 56.0 tok/s.
Qwen 3 4B (4B parameters) requires approximately 6.5 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 B570 10GB, Qwen 3 4B achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
For coding workloads, Qwen 3 4B on Intel Arc B570 10GB receives a S grade with 56.0 tok/s and 33K context.
On Intel Arc B570 10GB, Qwen 3 4B can safely use up to 33K tokens of context. The model's official context limit is 33K, 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3-4b-on-arc-b570-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A81 |
Q4_K_M | 4 | 2.4 GB | Medium | A82 |
Q5_K_M | 5 | 2.9 GB | High | A82 |
Q6_K | 6 | 3.3 GB | High | A83 |
Q8_0Best for your GPU | 8 | 4.3 GB | Very High | A84 |
F16 | 16 | 8.2 GB | Maximum | F0 |
 Nemotron Nano 8B | 8B | S | 45.2 tok/s |
 InternVL2 8B | 8B | A | 45.2 tok/s |
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.