Will It Run AI

Can Qwen 3.5 35B A3B run on Intel Arc Pro B60 24GB?

BARELY — Tight on Memory

A81Great
Estimated from fit model

Qwen 3.5 35B A3B needs ~26.1 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
Share:

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) — 26.1 GB, 21.9 tok/s, Very compromised (needs ~1.7 GB host RAM)
26.1 GB required24.0 GB available
109% VRAM needed

2.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.7 GB host RAM)

Decode

21.9 tok/s

TTFT

8838 ms

Safe context

4K

Memory

26.1 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights21.3 GB
KV Cache1.5 GB
Runtime0.9 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 3.5 35B A3B 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: 21.9 tok/s decode · 8.8s TTFT (warm) · 55 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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
ChatARuns with offload (needs ~1.2 GB host RAM)23.2 tok/s4549 ms4K
CodingAVery compromised (needs ~1.7 GB host RAM)21.9 tok/s8838 ms4K
Agentic CodingAVery compromised (needs ~2.8 GB host RAM)19.6 tok/s14369 ms4K
ReasoningAVery compromised (needs ~1.7 GB host RAM)21.9 tok/s10445 ms4K
RAGAVery compromised (needs ~2.8 GB host RAM)19.6 tok/s17962 ms4K

Quantization options

How Qwen 3.5 35B A3B (35B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.7 GB
LowS92
Q3_K_SBest for your GPU
3
17.2 GB
LowS91
NVFP4
4
19.6 GB
MediumF0
Q4_K_M
4
21.3 GB
MediumF0
Q5_K_M
5
25.2 GB
HighF0
Q6_K
6
28.7 GB
HighF0
Q8_0
8
37.5 GB
Very HighF0
F16
16
71.8 GB
MaximumF0

Get started

Copy-paste commands to run Qwen 3.5 35B A3B on your machine.

Run

ollama run qwen3.5:35b-a3b

Frequently asked questions

Can Intel Arc Pro B60 24GB run Qwen 3.5 35B A3B?

Yes, Intel Arc Pro B60 24GB can run Qwen 3.5 35B A3B with a A grade (Very compromised (needs ~1.7 GB host RAM)). Expected decode speed: 21.9 tok/s.

How much VRAM does Qwen 3.5 35B A3B need?

Qwen 3.5 35B A3B (35B parameters) requires approximately 26.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.5 35B A3B?

The recommended quantization for Qwen 3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.5 35B A3B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.5 35B A3B achieves approximately 21.9 tokens per second decode speed with a time-to-first-token of 8838ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Qwen 3.5 35B A3B for coding?

For coding workloads, Qwen 3.5 35B A3B on Intel Arc Pro B60 24GB receives a A grade with 21.9 tok/s and 4K context.

What context window can Qwen 3.5 35B A3B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.5 35B A3B can safely use up to 4K 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 35B A3B feels slow on Intel Arc Pro B60 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Qwen 3.5 35B A3B?

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 Qwen 3.5 35B A3B
Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-3.5-35b-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: