Can Qwen 2.5 Coder 14B run on MacBook Pro M3 Pro 36GB?

YES — Runs Great

B64Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~16.3 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~14 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) — 16.3 GB, 13.8 tok/s, Runs well

16.3 GB required25.9 GB available

63% VRAM used

Fit status

Runs well

Decode

13.8 tok/s

TTFT

13981 ms

Safe context

69K

Memory

16.3 GB / 25.9 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on MacBook Pro M3 Pro 36GB
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: 13.8 tok/s decode · 14.0s TTFT (warm) · 35 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	13.8 tok/s	7626 ms	69K
Coding	B	Runs well	13.8 tok/s	13981 ms	69K
Agentic Coding	B	Runs well	13.8 tok/s	20335 ms	69K
Reasoning	B	Runs well	13.8 tok/s	16523 ms	69K
RAG	B	Runs well	13.8 tok/s	25419 ms	69K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B59
Q3_K_S	3	6.9 GB	Low	B60
NVFP4	4	7.8 GB	Medium	B61
Q4_K_M	4	8.5 GB	Medium	B61
Q5_K_M	5	10.1 GB	High	B62
Q6_K	6	11.5 GB	High	B63
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

Opciones de mejora

Hardware que ejecuta bien Qwen 2.5 Coder 14B

MacBook Pro M4 Max 48GBOpción económica

48 GB Unified (+12)546 GB/s (+396)

Sube la velocidad estimada de decodificación alrededor de un 178%.38.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 178%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Mac Studio M2 Ultra 64GBMejor relación calidad-precio

64 GB Unified (+28)800 GB/s (+650)

Sube la velocidad estimada de decodificación alrededor de un 325%.58.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 325%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Pro 36GB run Qwen 2.5 Coder 14B?

Yes, MacBook Pro M3 Pro 36GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 13.8 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 16.3 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 M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Qwen 2.5 Coder 14B achieves approximately 13.8 tokens per second decode speed with a time-to-first-token of 13981ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 36GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on MacBook Pro M3 Pro 36GB receives a B grade with 13.8 tok/s and 69K context.

What context window can Qwen 2.5 Coder 14B use on MacBook Pro M3 Pro 36GB?

On MacBook Pro M3 Pro 36GB, Qwen 2.5 Coder 14B can safely use up to 69K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Pro 36GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. MacBook Pro M3 Pro 36GB 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 M3 Pro 36GB See all hardware for Qwen 2.5 Coder 14B

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