Can Qwen 2.5 Coder 7B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

B65Good

○Estimated from fit model

Qwen 2.5 Coder 7B needs ~16.4 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~59 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) — 16.4 GB, 59.0 tok/s, Runs well

16.4 GB required69.1 GB available

24% VRAM used

Fit status

Runs well

Decode

59.0 tok/s

TTFT

3282 ms

Safe context

131K

Memory

16.4 GB / 69.1 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on MacBook Pro M2 Max 96GB
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: 59.0 tok/s decode · 3.3s TTFT (warm) · 148 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	B	Runs well	59.0 tok/s	1790 ms	131K
Coding	B	Runs well	59.0 tok/s	3282 ms	131K
Agentic Coding	B	Runs well	59.0 tok/s	4774 ms	131K
Reasoning	B	Runs well	59.0 tok/s	3879 ms	131K
RAG	B	Runs well	59.0 tok/s	5967 ms	131K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B60
Q3_K_S	3	3.4 GB	Low	B60
NVFP4	4	3.9 GB	Medium	B60
Q4_K_M	4	4.3 GB	Medium	B60
Q5_K_M	5	5.0 GB	High	B60
Q6_K	6	5.7 GB	High	B60
Q8_0	8	7.5 GB	Very High	B60
F16Best for your GPU	16	14.3 GB	Maximum	B61