Can Qwen 2.5 Coder 14B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

B61Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~26.2 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 26.2 GB, 58.7 tok/s, Runs well

26.2 GB required92.2 GB available

28% VRAM used

Fit status

Runs well

Decode

58.7 tok/s

TTFT

3299 ms

Safe context

131K

Memory

26.2 GB / 92.2 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on Mac Studio M2 Ultra 128GB
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: 58.7 tok/s decode · 3.3s TTFT (warm) · 147 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	58.7 tok/s	1800 ms	131K
Coding	B	Runs well	54.3 tok/s	3563 ms	131K
Agentic Coding	B	Runs well	58.7 tok/s	4799 ms	131K
Reasoning	B	Runs well	58.7 tok/s	3899 ms	131K
RAG	B	Runs well	58.7 tok/s	5999 ms	131K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C54
Q3_K_S	3	6.9 GB	Low	C54
NVFP4	4	7.8 GB	Medium	C54
Q4_K_M	4	8.5 GB	Medium	C54
Q5_K_M	5	10.1 GB	High	C54
Q6_K	6	11.5 GB	High	C54
Q8_0	8	15.0 GB	Very High	C54
F16Best for your GPU	16	28.7 GB	Maximum	B56

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

RTX PRO 6000 Blackwell Workstation Edition 96GBOpción económica

1792 GB/s (+992)

Sube la velocidad estimada de decodificación alrededor de un 224%.190.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 224%.

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBMejor relación calidad-precio

1597 GB/s (+797)

Sube la velocidad estimada de decodificación alrededor de un 189%.169.6 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 189%.

~$9,999 MSRP

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Qwen 2.5 Coder 14B?

Yes, Mac Studio M2 Ultra 128GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 54.3 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 26.2 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 Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen 2.5 Coder 14B achieves approximately 54.3 tokens per second decode speed with a time-to-first-token of 3563ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Mac Studio M2 Ultra 128GB receives a B grade with 54.3 tok/s and 131K context.

What context window can Qwen 2.5 Coder 14B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Qwen 2.5 Coder 14B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. Mac Studio M2 Ultra 128GB 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 Mac Studio M2 Ultra 128GB See all hardware for Qwen 2.5 Coder 14B

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