Can Qwen 2.5 Coder 3B run on MacBook Air M1 16GB?

YES — Runs Great

A76Great

○Estimated from fit model

Qwen 2.5 Coder 3B needs ~6.7 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 6.7 GB, 23.4 tok/s, Runs well

6.7 GB required11.5 GB available

58% VRAM used

Fit status

Runs well

Decode

23.4 tok/s

TTFT

8270 ms

Safe context

51K

Memory

6.7 GB / 11.5 GB

Memory breakdown

Weights1.8 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 3B on MacBook Air M1 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: 23.4 tok/s decode · 8.3s TTFT (warm) · 59 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	A	Runs well	23.4 tok/s	4511 ms	51K
Coding	A	Runs well	23.4 tok/s	8270 ms	51K
Agentic Coding	A	Runs well	23.4 tok/s	12029 ms	51K
Reasoning	A	Runs well	23.4 tok/s	9774 ms	51K
RAG	A	Runs well	23.4 tok/s	15036 ms	51K

Quantization options

How Qwen 2.5 Coder 3B (3B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	A73
Q3_K_S	3	1.5 GB	Low	A74
NVFP4	4	1.7 GB	Medium	A74
Q4_K_M	4	1.8 GB	Medium	A74
Q5_K_M	5	2.2 GB	High	A74
Q6_K	6	2.5 GB	High	A75
Q8_0	8	3.2 GB	Very High	A76
F16Best for your GPU	16	6.1 GB	Maximum	A78

Get started

Copy-paste commands to run Qwen 2.5 Coder 3B on your machine.

Run

ollama run qwen2.5-coder:3b

Your hardware

More models your MacBook Air M1 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	8 tok/s
👁 Alibaba Qwen 3 14B	14B	B	4 tok/s
👁 Alibaba Qwen 3.5 4B	4B	S	18 tok/s
👁 Alibaba Qwen 3 8B	8B	S	9 tok/s
👁 Microsoft Phi-4 Mini Reasoning 4B	3.8B	S	18.9 tok/s

Frequently asked questions

See all results for MacBook Air M1 16GB See all hardware for Qwen 2.5 Coder 3B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-3b-on-m1-16gb

⇱ Qwen 2.5 Coder 3B on MacBook Air M1 16GB? YES