Would CUDA be a better path than Intel Arc Pro B60 24GB for Qwen 3.5 9B?

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.

Can Qwen 3.5 9B run on Intel Arc Pro B60 24GB?

Q: What should I upgrade first if Qwen 3.5 9B 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.

YES — Runs Great

S91Excellent

Estimated from fit model

Qwen 3.5 9B needs ~11.0 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

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

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) — 11.0 GB, 48.2 tok/s, Runs well

11.0 GB required24.0 GB available

46% VRAM used

Fit status

Runs well

Decode

48.2 tok/s

TTFT

4015 ms

Safe context

111K

Memory

11.0 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B 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: 48.2 tok/s decode · 4.0s TTFT (warm) · 121 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	48.2 tok/s	2190 ms	111K
Coding	S	Runs well	48.2 tok/s	4015 ms	111K
Agentic Coding	S	Runs well	48.2 tok/s	5840 ms	111K
Reasoning	S	Runs well	48.2 tok/s	4745 ms	111K
RAG	S	Runs well	48.2 tok/s	7300 ms	111K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S86
Q3_K_S	3	4.4 GB	Low	S87
NVFP4	4

Get started

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

Run

ollama run qwen3.5:9b

Your hardware

More models your Intel Arc Pro B60 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	37.2 tok/s
Qwen 3.5 27B	27B	S	16.1 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Qwen 3.5 9B?

Yes, Intel Arc Pro B60 24GB can run Qwen 3.5 9B with a S grade (Runs well). Expected decode speed: 48.2 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 11.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 9B?

The recommended quantization for Qwen 3.5 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 9B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.5 9B achieves approximately 48.2 tokens per second decode speed with a time-to-first-token of 4015ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on Intel Arc Pro B60 24GB receives a S grade with 48.2 tok/s and 111K context.

What context window can Qwen 3.5 9B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.5 9B can safely use up to 111K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.5 9B feels slow on Intel Arc Pro B60 24GB?

See all results for Intel Arc Pro B60 24GB See all hardware for Qwen 3.5 9B

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