Can Qwen 2.5 Coder 32B run on MacBook Pro M4 Pro 64GB?

YES — Runs Great

A79Great

▲Estimated — low-sample bucket· few comparable runs

Qwen 2.5 Coder 32B needs ~31.2 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 31.2 GB, 20.9 tok/s, Runs well

31.2 GB required46.1 GB available

68% VRAM used

Fit status

Runs well

Decode

20.9 tok/s

TTFT

9248 ms

Safe context

77K

Memory

31.2 GB / 46.1 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 32B on MacBook Pro M4 Pro 64GB
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: 20.9 tok/s decode · 9.2s TTFT (warm) · 52 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	20.9 tok/s	5044 ms	77K
Coding	A	Runs well	20.9 tok/s	9248 ms	77K
Agentic Coding	A	Runs well	20.9 tok/s	13451 ms	77K
Reasoning	A	Runs well	20.9 tok/s	10929 ms	77K
RAG	A	Runs well	20.9 tok/s	16814 ms	77K

Quantization options

How Qwen 2.5 Coder 32B (32B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A72
Q3_K_S	3	15.7 GB	Low	A73
NVFP4	4

Get started

Copy-paste commands to run Qwen 2.5 Coder 32B on your machine.

Run

ollama run qwen2.5-coder

Your hardware

More models your MacBook Pro M4 Pro 64GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	29.4 tok/s
👁 Alibaba

Frequently asked questions

See all results for MacBook Pro M4 Pro 64GB See all hardware for Qwen 2.5 Coder 32B

URL: https://willitrunai.com/can-run/qwen-2.5-coder-32b-on-m4-pro-64gb

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