Can Qwen3-VL 30B A3B Instruct run on Intel Arc Pro B60 24GB?
YES — With Offload
Qwen3-VL 30B A3B Instruct needs ~23.6 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Operating mode
Choose the run profile you care about
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
38.5 tok/s
TTFT
5028 ms
Safe context
21K
Memory
23.6 GB / 24.0 GB
Memory breakdown
See how fast it feels
What limits this setup
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.
Best improvement path
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.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs with offload | 38.5 tok/s | 2742 ms | 21K |
| Coding | S | Runs with offload | 38.5 tok/s | 5028 ms | 21K |
| Agentic Coding | S | Runs with offload (needs ~0.8 GB host RAM) | 27.0 tok/s | 10418 ms | 21K |
| Reasoning | S | Runs with offload | 38.5 tok/s | 5942 ms | 21K |
| RAG | S | Runs with offload (needs ~0.8 GB host RAM) | 27.0 tok/s | 13023 ms | 21K |
Quantization options
How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | S93 |
Q3_K_S | 3 | 14.7 GB | Low | S92 |
NVFP4 | 4 | 16.8 GB | Medium | S92 |
Q4_K_MBest for your GPU | 4 | 18.3 GB | Medium | S92 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen3-VL 30B A3B Instruct on your machine.
Run
lms load Qwen3-VL-30B-A3B-Instruct && lms server startYour hardware
More models your Intel Arc Pro B60 24GB can run
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 👁 Alibaba Qwen3-Coder 30B A3B Instruct | 30.5B | S | 37.2 tok/s |
