Can Qwen 2.5 Coder 14B run on Mac mini M4 64GB?

YES — Runs Great

B58Good

Estimated — low-sample bucket· few comparable runs

Qwen 2.5 Coder 14B needs ~19.3 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 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) — 19.3 GB, 9.6 tok/s, Runs well

19.3 GB required46.1 GB available

42% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20135 ms

Safe context

131K

Memory

19.3 GB / 46.1 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on Mac mini M4 64GB
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: 9.6 tok/s decode · 20.1s TTFT (warm) · 24 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	9.6 tok/s	10983 ms	131K
Coding	B	Runs well	9.6 tok/s	20135 ms	131K
Agentic Coding	B	Runs well	9.6 tok/s	29287 ms	131K
Reasoning	B	Runs well	9.6 tok/s	23795 ms	131K
RAG	B	Runs well	9.6 tok/s	36608 ms	131K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B56
Q3_K_S	3	6.9 GB	Low	B57
NVFP4	4	7.8 GB	Medium	B57
Q4_K_M	4	8.5 GB	Medium	B57
Q5_K_M	5	10.1 GB	High	B57
Q6_K	6	11.5 GB	High	B58
Q8_0	8	15.0 GB	Very High	B59
F16Best for your GPU	16	28.7 GB	Maximum	B62

Get started

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

Run

ollama run qwen2.5-coder:14b

升级选项

能流畅运行 Qwen 2.5 Coder 14B 的硬件

MacBook Pro M4 Max 96GB经济之选

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 299%.38.3 tok/s 解码

Raises estimated decode speed by about 299%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

MacBook Pro M3 Max 128GB最佳性价比

128 GB Unified (+64)400 GB/s (+280)

Raises estimated decode speed by about 217%.30.4 tok/s 解码

Raises estimated decode speed by about 217%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBApple 升级

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 633%.70.4 tok/s 解码

Raises estimated decode speed by about 633%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Qwen 2.5 Coder 14B?

Yes, Mac mini M4 64GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 9.6 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 19.3 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 Mac mini M4 64GB?

On Mac mini M4 64GB, Qwen 2.5 Coder 14B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20135ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Mac mini M4 64GB receives a B grade with 9.6 tok/s and 131K context.

What context window can Qwen 2.5 Coder 14B use on Mac mini M4 64GB?

On Mac mini M4 64GB, 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 mini M4 64GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. Mac mini M4 64GB 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 mini M4 64GB See all hardware for Qwen 2.5 Coder 14B

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