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 ~35 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
38.5 tok/s
TTFT
5028 ms
Safe context
21K
Memory
23.6 GB / 24.0 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 | S | Runs with offload | 35.4 tok/s | 2982 ms | 21K |
| Coding | S | Runs with offload | 35.4 tok/s | 5467 ms | 21K |
| Agentic Coding | S | Runs with offload | 24.9 tok/s | 11330 ms | 21K |
| Reasoning | S | Runs with offload | 35.4 tok/s | 6461 ms | 21K |
| RAG | S | Runs with offload | 24.9 tok/s | 14163 ms | 21K |
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 |
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
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 37.2 tok/s |
Yes, Intel Arc Pro B60 24GB can run Qwen3-VL 30B A3B Instruct with a S grade (Runs with offload). Expected decode speed: 35.4 tok/s.
Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 23.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, Qwen3-VL 30B A3B Instruct achieves approximately 35.4 tokens per second decode speed with a time-to-first-token of 5467ms using Q4_K_M quantization.
For coding workloads, Qwen3-VL 30B A3B Instruct on Intel Arc Pro B60 24GB receives a S grade with 35.4 tok/s and 21K context.
On Intel Arc Pro B60 24GB, Qwen3-VL 30B A3B Instruct can safely use up to 21K tokens of context. The model's official context limit is 256K, 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-vl-30b-a3b-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
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.