Will It Run AI

Can Qwen3-Coder 30B A3B Instruct run on Intel Arc Pro B60 24GB?

YES — With Offload

S94Excellent
Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~23.4 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~37 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.4 GB, 37.2 tok/s, Runs with offload
23.4 GB required24.0 GB available
98% VRAM used

Fit status

Runs with offload

Decode

37.2 tok/s

TTFT

5199 ms

Safe context

23K

Memory

23.4 GB / 24.0 GB

Memory breakdown

Weights18.6 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on Intel Arc Pro B60 24GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 37.2 tok/s decode · 5.2s TTFT (warm) · 93 tok/s prefill

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

WorkloadGradeFitDecodeTTFTContext
ChatSTight fit37.2 tok/s2836 ms23K
CodingSRuns with offload37.2 tok/s5199 ms23K
Agentic CodingSRuns with offload (needs ~0.6 GB host RAM)26.6 tok/s10604 ms23K
ReasoningSRuns with offload37.2 tok/s6145 ms23K
RAGSRuns with offload (needs ~0.6 GB host RAM)26.6 tok/s13255 ms23K

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.9 GB
LowS93
Q3_K_S
3
14.9 GB
LowS93
NVFP4
4
17.1 GB
MediumS93
Q4_K_MBest for your GPU
4
18.6 GB
MediumS92
Q5_K_M
5
22.0 GB
HighF0
Q6_K
6
25.0 GB
HighF0
Q8_0
8
32.6 GB
Very HighF0
F16
16
62.5 GB
MaximumF0

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Frequently asked questions

Can Intel Arc Pro B60 24GB run Qwen3-Coder 30B A3B Instruct?

Yes, Intel Arc Pro B60 24GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs with offload). Expected decode speed: 37.2 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 23.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen3-Coder 30B A3B Instruct achieves approximately 37.2 tokens per second decode speed with a time-to-first-token of 5199ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on Intel Arc Pro B60 24GB receives a S grade with 37.2 tok/s and 23K context.

What context window can Qwen3-Coder 30B A3B Instruct use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen3-Coder 30B A3B Instruct can safely use up to 23K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3-Coder 30B A3B Instruct feels slow on Intel Arc Pro B60 24GB?

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.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Qwen3-Coder 30B A3B Instruct?

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.

See all results for Intel Arc Pro B60 24GBSee all hardware for Qwen3-Coder 30B A3B Instruct
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