Can Qwen 3.6 35B A3B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

S91Excellent

Estimated from fit model

Qwen 3.6 35B A3B needs ~40.1 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~256 tok/s.

Runtime: TransformersCapacity: RoomyBandwidth: HighStack: 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) — 40.1 GB, 256.2 tok/s, Runs well

40.1 GB required128.0 GB available

31% VRAM used

Fit status

Runs well

Decode

256.2 tok/s

TTFT

756 ms

Safe context

262K

Memory

40.1 GB / 128.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.8 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsQwen 3.6 35B A3B on Intel Data Center GPU Max 1550 128GB
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: 256.2 tok/s decode · 756ms TTFT (warm) · 641 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	256.2 tok/s	412 ms	262K
Coding	S	Runs well	256.2 tok/s	756 ms	262K
Agentic Coding	S	Runs well	256.2 tok/s	1099 ms	262K
Reasoning	S	Runs well	256.2 tok/s	893 ms	262K
RAG	S	Runs well	256.2 tok/s	1374 ms	262K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A81
Q3_K_S	3	17.2 GB	Low	A81
NVFP4	4	19.6 GB	Medium	A81
Q4_K_M	4	21.3 GB	Medium	A81
Q5_K_M	5	25.2 GB	High	A82
Q6_K	6	28.7 GB	High	A83
Q8_0	8	37.5 GB	Very High	A84
F16Best for your GPU	16	71.8 GB	Maximum	S90

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

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen 3.5 122B A10B	122B	S	81 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Qwen 3.6 35B A3B?

Yes, Intel Data Center GPU Max 1550 128GB can run Qwen 3.6 35B A3B with a S grade (Runs well). Expected decode speed: 256.2 tok/s.

How much VRAM does Qwen 3.6 35B A3B need?

Qwen 3.6 35B A3B (35B parameters) requires approximately 40.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 35B A3B?

The recommended quantization for Qwen 3.6 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 35B A3B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen 3.6 35B A3B achieves approximately 256.2 tokens per second decode speed with a time-to-first-token of 756ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Qwen 3.6 35B A3B for coding?

For coding workloads, Qwen 3.6 35B A3B on Intel Data Center GPU Max 1550 128GB receives a S grade with 256.2 tok/s and 262K context.

What context window can Qwen 3.6 35B A3B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Qwen 3.6 35B A3B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 3.6 35B A3B feels slow on Intel Data Center GPU Max 1550 128GB?

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 Data Center GPU Max 1550 128GB for Qwen 3.6 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 Data Center GPU Max 1550 128GB See all hardware for Qwen 3.6 35B A3B

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<iframe src="https://willitrunai.com/embed/qwen-3.6-35b-a3b-on-max-1550-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

256.2 tok/s

412 ms

262K

Coding

Runs well

256.2 tok/s

756 ms

262K

Agentic Coding

Runs well

256.2 tok/s

1099 ms

262K

Reasoning

Runs well

256.2 tok/s

893 ms

262K

RAG

Runs well

256.2 tok/s

1374 ms

262K

Quantization options

How Qwen 3.6 35B A3B (35B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	A81
Q3_K_S	3	17.2 GB	Low	A81
NVFP4	4	19.6 GB	Medium	A81
Q4_K_M	4	21.3 GB	Medium	A81
Q5_K_M	5	25.2 GB	High	A82
Q6_K	6	28.7 GB	High	A83
Q8_0	8	37.5 GB	Very High	A84
F16Best for your GPU	16	71.8 GB	Maximum	S90

Model

Params

Grade

Decode

Capabilities

Devstral 2 123B Instruct

123B

29.2 tok/s

Qwen 3.5 122B A10B

122B

81 tok/s