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

YES — Tight Fit

S92Excellent

○Estimated from fit model

Qwen 3.5 9B needs ~10.3 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~27 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, 27.4 tok/s, Tight fit

10.3 GB required11.5 GB available

90% VRAM used

Fit status

Tight fit

Decode

27.4 tok/s

TTFT

7062 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 Pro M2 Pro 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: 27.4 tok/s decode · 7.1s TTFT (warm) · 69 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	27.4 tok/s	3852 ms	25K
Coding	S	Tight fit	27.4 tok/s	7062 ms	25K
Agentic Coding	A	Very compromised (needs ~0.4 GB host RAM)	23.8 tok/s	11830 ms	25K
Reasoning	S	Tight fit	27.4 tok/s	8346 ms	25K
RAG	A	Very compromised (needs ~0.4 GB host RAM)	23.8 tok/s	14788 ms	25K

Quantization options

How Qwen 3.5 9B (9B params) fits at each quantization level on MacBook Pro M2 Pro 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

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

URL: https://willitrunai.com/can-run/qwen-3.5-9b-on-m2-pro-16gb

⇱ Qwen 3.5 9B on MacBook Pro M2 Pro 16GB? TIGHT FIT