Can Qwen 2.5 Coder 7B run on Mac mini M2 24GB?

YES — Runs Great

B67Good

Estimated from fit model

Qwen 2.5 Coder 7B needs ~8.6 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~17 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) — 8.6 GB, 16.5 tok/s, Runs well

8.6 GB required17.3 GB available

50% VRAM used

Fit status

Runs well

Decode

16.5 tok/s

TTFT

11714 ms

Safe context

131K

Memory

8.6 GB / 17.3 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on Mac mini M2 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: 16.5 tok/s decode · 11.7s TTFT (warm) · 41 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	16.5 tok/s	6389 ms	131K
Coding	B	Runs well	16.5 tok/s	11714 ms	131K
Agentic Coding	B	Runs well	16.5 tok/s	17039 ms	131K
Reasoning	B	Runs well	16.5 tok/s	13844 ms	131K
RAG	B	Runs well	16.5 tok/s	21298 ms	131K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B66
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4	3.9 GB	Medium	B67
Q4_K_M	4	4.3 GB	Medium	B67
Q5_K_M	5	5.0 GB	High	B68
Q6_K	6	5.7 GB	High	B69
Q8_0Best for your GPU	8	7.5 GB	Very High	A70
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 7B on your machine.

Run

ollama run qwen2.5-coder:7b

Upgrade options

Hardware that runs Qwen 2.5 Coder 7B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 258%.59 tok/s decode

Raises estimated decode speed by about 258%.

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

~$1,999 MSRP

MacBook Pro M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 116%.35.6 tok/s decode

Raises estimated decode speed by about 116%.

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

~$1,999 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+40)800 GB/s (+700)

Raises estimated decode speed by about 494%.98 tok/s decode

Raises estimated decode speed by about 494%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M2 24GB run Qwen 2.5 Coder 7B?

Yes, Mac mini M2 24GB can run Qwen 2.5 Coder 7B with a B grade (Runs well). Expected decode speed: 16.5 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 7B?

The recommended quantization for Qwen 2.5 Coder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 7B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Qwen 2.5 Coder 7B achieves approximately 16.5 tokens per second decode speed with a time-to-first-token of 11714ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on Mac mini M2 24GB receives a B grade with 16.5 tok/s and 131K context.

What context window can Qwen 2.5 Coder 7B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Qwen 2.5 Coder 7B 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 M2 24GB as fast as VRAM for Qwen 2.5 Coder 7B?

Not always. Mac mini M2 24GB 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 M2 24GB See all hardware for Qwen 2.5 Coder 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-7b-on-m2-24gb" 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

16.5 tok/s

6389 ms

131K

Coding

Runs well

16.5 tok/s

11714 ms

131K

Agentic Coding

Runs well

16.5 tok/s

17039 ms

131K

Reasoning

Runs well

16.5 tok/s

13844 ms

131K

RAG

Runs well

16.5 tok/s

21298 ms

131K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B66
Q3_K_S	3	3.4 GB	Low	B67
NVFP4	4	3.9 GB	Medium	B67
Q4_K_M	4	4.3 GB	Medium	B67
Q5_K_M	5	5.0 GB	High	B68
Q6_K	6	5.7 GB	High	B69
Q8_0Best for your GPU	8	7.5 GB	Very High	A70
F16	16	14.3 GB	Maximum	F0

Frequently asked questions

Can Mac mini M2 24GB run Qwen 2.5 Coder 7B?

Yes, Mac mini M2 24GB can run Qwen 2.5 Coder 7B with a B grade (Runs well). Expected decode speed: 16.5 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 7B?

The recommended quantization for Qwen 2.5 Coder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 7B run at on Mac mini M2 24GB?

On Mac mini M2 24GB, Qwen 2.5 Coder 7B achieves approximately 16.5 tokens per second decode speed with a time-to-first-token of 11714ms using Q4_K_M quantization.

Can Mac mini M2 24GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on Mac mini M2 24GB receives a B grade with 16.5 tok/s and 131K context.

What context window can Qwen 2.5 Coder 7B use on Mac mini M2 24GB?

On Mac mini M2 24GB, Qwen 2.5 Coder 7B 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 M2 24GB as fast as VRAM for Qwen 2.5 Coder 7B?