Can Qwen 2.5 Coder 14B run on MacBook Pro M4 Pro 24GB?

YES — Tight Fit

B64Good

▲Estimated — low-sample bucket· few comparable runs

Qwen 2.5 Coder 14B needs ~15.0 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 15.0 GB, 23.4 tok/s, Tight fit

15.0 GB required17.3 GB available

87% VRAM used

Fit status

Tight fit

Decode

23.4 tok/s

TTFT

8276 ms

Safe context

29K

Memory

15.0 GB / 17.3 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on MacBook Pro M4 Pro 24GB
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	B	Runs well	24.6 tok/s	4290 ms	29K
Coding	B	Tight fit	24.6 tok/s	7865 ms	29K
Agentic Coding	B	Runs with offload	23.0 tok/s	12264 ms	29K
Reasoning	B	Tight fit	24.6 tok/s	9295 ms	29K
RAG	B	Runs with offload	23.0 tok/s	15329 ms	29K