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URL: https://willitrunai.com/can-run/qwen-3.6-35b-a3b-on-arc-pro-b60-24gb

⇱ Qwen 3.6 35B A3B on Intel Arc Pro B60 24GB? No — Alternativ…

Can Qwen 3.6 35B A3B run on Intel Arc Pro B60 24GB?

YES — With NVFP4

A81Great
○Estimated from fit model

Qwen 3.6 35B A3B needs ~27.9 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With NVFP4 quantization, expect ~20 tok/s.

Runtime: TransformersCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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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.

Qwen 3.6 35B A3B at Q4_K_M needs 29.7 GB — too much for Intel Arc Pro B60 24GB (24.0 GB). Runs at NVFP4 (27.9 GB) with medium quality. 3 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 29.7 GB, exceeds 24.0 GB available
29.7 GB required24.0 GB available
124% VRAM needed

5.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.5 tok/s

TTFT

12454 ms

Safe context

4K

Memory

29.7 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights21.3 GB
KV Cache4.1 GB
Runtime1.8 GB
Headroom2.4 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen 3.6 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: 15.5 tok/s decode · 12.5s TTFT (warm) · 39 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
ChatAVery compromised (needs ~2.8 GB host RAM)18.0 tok/s5869 ms4K
CodingFToo heavy15.5 tok/s12454 ms4K
Agentic CodingFToo heavy11.9 tok/s23595 ms4K
ReasoningFToo heavy15.5 tok/s14719 ms4K
RAGFToo heavy11.9 tok/s29494 ms4K

Quantization options

How Qwen 3.6 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
LowS92
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 3.6 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "Qwen/Qwen3.6-35B-A3B" \ --hf-file "Qwen3.6-35B-A3B-Q4_K_M.gguf" \ -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen 3.6 35B A3B well

👁 NVIDIA
NVIDIA A100 40GBBiggest leap
40 GB VRAM (+16)1555 GB/s (+1099)
S
Makes the model fit on the accelerator instead of staying completely out of reach.166 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$10,000 MSRP

👁 Intel
Intel Data Center GPU Max 1550 128GBBudget pick
128 GB VRAM (+104)3200 GB/s (+2744)
S
Makes the model fit on the accelerator instead of staying completely out of reach.256.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

👁 Intel
Gaudi 3 128GBBest value
128 GB VRAM (+104)3700 GB/s (+3244)
S
Makes the model fit on the accelerator instead of staying completely out of reach.329.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$15,000 MSRP

Frequently asked questions

See all results for Intel Arc Pro B60 24GBSee all hardware for Qwen 3.6 35B A3B