Can Qwen 2.5 Coder 14B run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

B66Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.8 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~18 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) — 15.8 GB, 17.7 tok/s, Runs well

15.8 GB required23.0 GB available

69% VRAM used

Fit status

Runs well

Decode

17.7 tok/s

TTFT

10935 ms

Safe context

55K

Memory

15.8 GB / 23.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on MacBook Pro M2 Pro 32GB
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: 17.7 tok/s decode · 10.9s TTFT (warm) · 44 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	17.7 tok/s	5964 ms	55K
Coding	B	Runs well	17.7 tok/s	10935 ms	55K
Agentic Coding	B	Runs well	17.7 tok/s	15905 ms	55K
Reasoning	B	Runs well	17.7 tok/s	12923 ms	55K
RAG	B	Runs well	17.7 tok/s	19881 ms	55K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4	7.8 GB	Medium	B62
Q4_K_M	4	8.5 GB	Medium	B62
Q5_K_M	5	10.1 GB	High	B63
Q6_K	6	11.5 GB	High	B64
Q8_0Best for your GPU	8	15.0 GB	Very High	B64
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

Upgrade options

Hardware that runs Qwen 2.5 Coder 14B well

Intel Arc Pro B60 24GBBest value

456 GB/s (+256)

Raises estimated decode speed by about 76%.31.1 tok/s decode

Raises estimated decode speed by about 76%.

~$599 MSRP

MacBook Pro M4 Max 36GBBudget pick

36 GB Unified (+4)410 GB/s (+210)

Raises estimated decode speed by about 85%.32.7 tok/s decode

Raises estimated decode speed by about 85%.

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Qwen 2.5 Coder 14B?

Yes, MacBook Pro M2 Pro 32GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 17.7 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Qwen 2.5 Coder 14B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10935ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on MacBook Pro M2 Pro 32GB receives a B grade with 17.7 tok/s and 55K context.

What context window can Qwen 2.5 Coder 14B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Qwen 2.5 Coder 14B can safely use up to 55K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. MacBook Pro M2 Pro 32GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M2 Pro 32GB See all hardware for Qwen 2.5 Coder 14B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-14b-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

17.7 tok/s

5964 ms

55K

Coding

Runs well

17.7 tok/s

10935 ms

55K

Agentic Coding

Runs well

17.7 tok/s

15905 ms

55K

Reasoning

Runs well

17.7 tok/s

12923 ms

55K

RAG

Runs well

17.7 tok/s

19881 ms

55K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B60
Q3_K_S	3	6.9 GB	Low	B61
NVFP4	4	7.8 GB	Medium	B62
Q4_K_M	4	8.5 GB	Medium	B62
Q5_K_M	5	10.1 GB	High	B63
Q6_K	6	11.5 GB	High	B64
Q8_0Best for your GPU	8	15.0 GB	Very High	B64
F16	16	28.7 GB	Maximum	F0