Can Qwen 3.6 27B run on Intel Arc Pro B60 24GB?

YES — Tight Fit

S90Excellent

Estimated from fit model

Qwen 3.6 27B needs ~20.7 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 20.7 GB, 12.3 tok/s, Tight fit

20.7 GB required24.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

12.3 tok/s

TTFT

15772 ms

Safe context

69K

Memory

20.7 GB / 24.0 GB

Memory breakdown

Weights16.5 GB

KV Cache1.0 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen 3.6 27B 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: 12.3 tok/s decode · 15.8s TTFT (warm) · 31 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	Tight fit	12.3 tok/s	8603 ms	69K
Coding	S	Tight fit	12.3 tok/s	15772 ms	69K
Agentic Coding	S	Tight fit	12.3 tok/s	22941 ms	69K
Reasoning	S	Tight fit	12.3 tok/s	18640 ms	69K
RAG	S	Tight fit	12.3 tok/s	28677 ms	69K

Inference speed

Qwen 3.6 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.6 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~79 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	79.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	50.4	Tight
RTX 3090 24GB	24 GB	Q4_K_M	43.1	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	29.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	27.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	27.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	27.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	23.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	21.9	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	17.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	9.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.2	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	S92
Q3_K_S	3	13.2 GB	Low	S93
NVFP4	4	15.1 GB	Medium	S92
Q4_K_MBest for your GPU	4	16.5 GB	Medium	S92
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 3.6 27B on your machine.

Run

lms load Qwen3.6-27B && lms server start

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

Frequently asked questions

Can Intel Arc Pro B60 24GB run Qwen 3.6 27B?

Yes, Intel Arc Pro B60 24GB can run Qwen 3.6 27B with a S grade (Tight fit). Expected decode speed: 12.3 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 20.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 27B?

The recommended quantization for Qwen 3.6 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 27B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.6 27B achieves approximately 12.3 tokens per second decode speed with a time-to-first-token of 15772ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on Intel Arc Pro B60 24GB receives a S grade with 12.3 tok/s and 69K context.

What context window can Qwen 3.6 27B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.6 27B can safely use up to 69K 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 27B 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.

Would CUDA be a better path than Intel Arc Pro B60 24GB for Qwen 3.6 27B?

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 24GB See all hardware for Qwen 3.6 27B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3.6-27b-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:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

12.3 tok/s

8603 ms

69K

Coding

Tight fit

12.3 tok/s

15772 ms

69K

Agentic Coding

Tight fit

12.3 tok/s

22941 ms

69K

Reasoning

Tight fit

12.3 tok/s

18640 ms

69K

RAG

Tight fit

12.3 tok/s

28677 ms

69K

Qwen 3.6 27B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

79.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

50.4

Tight

RTX 3090 24GB

24 GB

Q4_K_M

43.1

Tight

RX 7900 XTX 24GB

24 GB

Q4_K_M

29.8

Tight

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

27.8

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

27.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

27.4

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

23.1

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

21.9

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

17.3

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

12.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

11.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

9.1

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.2

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.2

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Qwen 3.6 27B (27B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	S92
Q3_K_S	3	13.2 GB	Low	S93
NVFP4	4	15.1 GB	Medium	S92
Q4_K_MBest for your GPU	4	16.5 GB	Medium	S92
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
F16	16	55.4 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

37.2 tok/s

Frequently asked questions

Can Intel Arc Pro B60 24GB run Qwen 3.6 27B?

Yes, Intel Arc Pro B60 24GB can run Qwen 3.6 27B with a S grade (Tight fit). Expected decode speed: 12.3 tok/s.

How much VRAM does Qwen 3.6 27B need?

Qwen 3.6 27B (27B parameters) requires approximately 20.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 3.6 27B?

The recommended quantization for Qwen 3.6 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 3.6 27B run at on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.6 27B achieves approximately 12.3 tokens per second decode speed with a time-to-first-token of 15772ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Qwen 3.6 27B for coding?

For coding workloads, Qwen 3.6 27B on Intel Arc Pro B60 24GB receives a S grade with 12.3 tok/s and 69K context.

What context window can Qwen 3.6 27B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Qwen 3.6 27B can safely use up to 69K 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 27B feels slow on Intel Arc Pro B60 24GB?

Would CUDA be a better path than Intel Arc Pro B60 24GB for Qwen 3.6 27B?