Can Qwen 3.5 9B run on MacBook Air M2 16GB?

YES — Tight Fit

S89Excellent

Estimated from fit model

Qwen 3.5 9B needs ~10.3 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 10.3 GB, 12.7 tok/s, Tight fit

10.3 GB required11.5 GB available

90% VRAM used

Fit status

Tight fit

Decode

12.7 tok/s

TTFT

15211 ms

Safe context

25K

Memory

10.3 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsQwen 3.5 9B on MacBook Air M2 16GB
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: 12.7 tok/s decode · 15.2s TTFT (warm) · 32 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	12.7 tok/s	8297 ms	25K
Coding	S	Tight fit	12.7 tok/s	15211 ms	25K
Agentic Coding	A	Very compromised (needs ~0.4 GB host RAM)	11.1 tok/s	25480 ms	25K
Reasoning	S	Tight fit	12.7 tok/s	17977 ms	25K
RAG	A	Very compromised (needs ~0.4 GB host RAM)	11.1 tok/s	31850 ms	25K

Inference speed

Qwen 3.5 9B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 3.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 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	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	119.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	90.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	86.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	73.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	73.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	71.5	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.1	Fits
RTX 3060 12GB	12 GB	Q4_K_M	39.9	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	37.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	19.2	Heavy offload

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 3.5 9B (9B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S92
Q3_K_S	3	4.4 GB	Low	S93
NVFP4	4	5.0 GB	Medium	S94
Q4_K_M	4	5.5 GB	Medium	S94
Q5_K_M	5	6.5 GB	High	S94
Q6_KBest for your GPU	6	7.4 GB	High	S93
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

ollama run qwen3.5:9b

Frequently asked questions

Can MacBook Air M2 16GB run Qwen 3.5 9B?

Yes, MacBook Air M2 16GB can run Qwen 3.5 9B with a S grade (Tight fit). Expected decode speed: 12.7 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 10.3 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 MacBook Air M2 16GB?

On MacBook Air M2 16GB, Qwen 3.5 9B achieves approximately 12.7 tokens per second decode speed with a time-to-first-token of 15211ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on MacBook Air M2 16GB receives a S grade with 12.7 tok/s and 25K context.

What context window can Qwen 3.5 9B use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, Qwen 3.5 9B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M2 16GB as fast as VRAM for Qwen 3.5 9B?

Not always. MacBook Air M2 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Air M2 16GB See all hardware for Qwen 3.5 9B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-3.5-9b-on-m2-air-16gb" 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

12.7 tok/s

8297 ms

25K

Coding

Tight fit

12.7 tok/s

15211 ms

25K

Agentic Coding

Very compromised (needs ~0.4 GB host RAM)

11.1 tok/s

25480 ms

25K

Reasoning

Tight fit

12.7 tok/s

17977 ms

25K

RAG

Very compromised (needs ~0.4 GB host RAM)

11.1 tok/s

31850 ms

25K

Qwen 3.5 9B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

126.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

126.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

119.6

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

109.1

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

90.9

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

86.2

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

73.4

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

73.4

Fits

RTX 4070 12GB

12 GB

Q4_K_M

71.5

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

47.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

43.1

Fits

RTX 3060 12GB

12 GB

Q4_K_M

39.9

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

37.9

Fits

RTX 4060 8GB

8 GB

Q4_K_M

19.2

Heavy offload

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	S92
Q3_K_S	3	4.4 GB	Low	S93
NVFP4	4	5.0 GB	Medium	S94
Q4_K_M	4	5.5 GB	Medium	S94
Q5_K_M	5	6.5 GB	High	S94
Q6_KBest for your GPU	6	7.4 GB	High	S93
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Frequently asked questions

Can MacBook Air M2 16GB run Qwen 3.5 9B?

Yes, MacBook Air M2 16GB can run Qwen 3.5 9B with a S grade (Tight fit). Expected decode speed: 12.7 tok/s.

How much VRAM does Qwen 3.5 9B need?

Qwen 3.5 9B (9B parameters) requires approximately 10.3 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 MacBook Air M2 16GB?

On MacBook Air M2 16GB, Qwen 3.5 9B achieves approximately 12.7 tokens per second decode speed with a time-to-first-token of 15211ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run Qwen 3.5 9B for coding?

For coding workloads, Qwen 3.5 9B on MacBook Air M2 16GB receives a S grade with 12.7 tok/s and 25K context.

What context window can Qwen 3.5 9B use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, Qwen 3.5 9B can safely use up to 25K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M2 16GB as fast as VRAM for Qwen 3.5 9B?