Can Qwen 3.5 9B run on Intel Arc Pro B50 16GB?

YES — Runs Great

S93Excellent

○Estimated from fit model

Qwen 3.5 9B needs ~10.2 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 10.2 GB, 23.7 tok/s, Runs well

10.2 GB required16.0 GB available

64% VRAM used

Fit status

Runs well

Decode

23.7 tok/s

TTFT

8174 ms

Safe context

58K

Memory

10.2 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on Intel Arc Pro B50 16GB
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: 23.7 tok/s decode · 8.2s TTFT (warm) · 59 tok/s prefill

What limits this setup

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.

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	23.7 tok/s	4458 ms	58K
Coding	S	Runs well	23.7 tok/s	8174 ms	58K
Agentic Coding	S	Runs well	23.7 tok/s	11889 ms	58K
Reasoning	S	Runs well	23.7 tok/s	9660 ms	58K
RAG	S	Runs well	23.7 tok/s	14862 ms	58K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S89
Q3_K_S	3	4.4 GB	Low	S90
NVFP4	4	5.0 GB	Medium	S90
Q4_K_M	4	5.5 GB	Medium	S91
Q5_K_M	5	6.5 GB	High	S92
Q6_K	6	7.4 GB	High	S93
Q8_0Best for your GPU	8	9.6 GB	Very High	S93
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.5 9B on your machine.

Run

ollama run qwen3.5:9b

Frequently asked questions

See all results for Intel Arc Pro B50 16GB See all hardware for Qwen 3.5 9B

URL: https://willitrunai.com/can-run/qwen-3.5-9b-on-arc-pro-b50-16gb

⇱ Qwen 3.5 9B on Intel Arc Pro B50 16GB? YES