Can Qwen 2.5 Coder 7B run on MacBook Pro M4 16GB?

YES — Runs Great

A71Great

▲Estimated — low-sample bucket· few comparable runs

Qwen 2.5 Coder 7B needs ~7.8 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~20 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) — 7.8 GB, 20.2 tok/s, Runs well

7.8 GB required11.5 GB available

68% VRAM used

Fit status

Runs well

Decode

20.2 tok/s

TTFT

9579 ms

Safe context

87K

Memory

7.8 GB / 11.5 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on MacBook Pro M4 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: 20.2 tok/s decode · 9.6s TTFT (warm) · 51 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	20.2 tok/s	5225 ms	87K
Coding	A	Runs well	20.2 tok/s	9579 ms	87K
Agentic Coding	A	Runs well	20.2 tok/s	13933 ms	87K
Reasoning	A	Runs well	20.2 tok/s	11321 ms	87K
RAG	A	Runs well	20.2 tok/s	17416 ms	87K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B69
Q3_K_S	3	3.4 GB	Low	A70
NVFP4	4	3.9 GB	Medium	A71
Q4_K_M	4	4.3 GB	Medium	A71
Q5_K_M	5	5.0 GB	High	A72
Q6_K	6	5.7 GB	High	A72
Q8_0Best for your GPU	8	7.5 GB	Very High	A72
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 7B on your machine.

Run

ollama run qwen2.5-coder:7b

Your hardware

More models your MacBook Pro M4 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	15.6 tok/s
👁 Alibaba Qwen 3 14B	14B	A	7.5 tok/s
👁 Alibaba Qwen 3 8B	8B	S	17.5 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	18.9 tok/s
👁 Mistral Ministral 3 14B	14B	B	7.4 tok/s

Frequently asked questions

See all results for MacBook Pro M4 16GB See all hardware for Qwen 2.5 Coder 7B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-7b-on-m4-16gb

⇱ Qwen 2.5 Coder 7B on MacBook Pro M4 16GB? YES