Can Qwen 2.5 Coder 14B run on MacBook Pro M2 Max 96GB?

YES — Runs Great

B60Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~22.7 GB VRAM. MacBook Pro M2 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 22.7 GB, 29.3 tok/s, Runs well

22.7 GB required69.1 GB available

33% VRAM used

Fit status

Runs well

Decode

29.3 tok/s

TTFT

6599 ms

Safe context

131K

Memory

22.7 GB / 69.1 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on MacBook Pro M2 Max 96GB
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: 29.3 tok/s decode · 6.6s TTFT (warm) · 73 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	29.3 tok/s	3599 ms	131K
Coding	B	Runs well	29.3 tok/s	6599 ms	131K
Agentic Coding	B	Runs well	29.3 tok/s	9598 ms	131K
Reasoning	B	Runs well	29.3 tok/s	7798 ms	131K
RAG	B	Runs well	29.3 tok/s	11997 ms	131K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C55
Q3_K_S	3	6.9 GB	Low	C55
NVFP4	4	7.8 GB	Medium	C55
Q4_K_M	4	8.5 GB	Medium	C55
Q5_K_M	5	10.1 GB	High	B55
Q6_K	6	11.5 GB	High	B55
Q8_0	8	15.0 GB	Very High	B56
F16Best for your GPU	16	28.7 GB	Maximum	B59

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

Mac Studio M2 Ultra 128GBBudget pick

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 100%.58.7 tok/s decode

Raises estimated decode speed by about 100%.

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

~$3,999 MSRP

Mac Studio M1 Ultra 128GBBest value

128 GB Unified (+32)800 GB/s (+400)

Raises estimated decode speed by about 90%.55.6 tok/s decode

Raises estimated decode speed by about 90%.

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

~$3,999 MSRP

Mac Studio M3 Ultra 256GBApple upgrade

256 GB Unified (+160)819 GB/s (+419)

Raises estimated decode speed by about 140%.70.4 tok/s decode

Raises estimated decode speed by about 140%.

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

~$6,999 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 96GB run Qwen 2.5 Coder 14B?

Yes, MacBook Pro M2 Max 96GB can run Qwen 2.5 Coder 14B with a B grade (Runs well). Expected decode speed: 29.3 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 22.7 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 Max 96GB?

On MacBook Pro M2 Max 96GB, Qwen 2.5 Coder 14B achieves approximately 29.3 tokens per second decode speed with a time-to-first-token of 6599ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 96GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on MacBook Pro M2 Max 96GB receives a B grade with 29.3 tok/s and 131K context.

What context window can Qwen 2.5 Coder 14B use on MacBook Pro M2 Max 96GB?

On MacBook Pro M2 Max 96GB, 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 MacBook Pro M2 Max 96GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. MacBook Pro M2 Max 96GB 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 Max 96GB See all hardware for Qwen 2.5 Coder 14B

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-14b-on-m2-max-96gb" 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

29.3 tok/s

3599 ms

131K

Coding

Runs well

29.3 tok/s

6599 ms

131K

Agentic Coding

Runs well

29.3 tok/s

9598 ms

131K

Reasoning

Runs well

29.3 tok/s

7798 ms

131K

RAG

Runs well

29.3 tok/s

11997 ms

131K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M2 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C55
Q3_K_S	3	6.9 GB	Low	C55
NVFP4	4	7.8 GB	Medium	C55
Q4_K_M	4	8.5 GB	Medium	C55
Q5_K_M	5	10.1 GB	High	B55
Q6_K	6	11.5 GB	High	B55
Q8_0	8	15.0 GB	Very High	B56
F16Best for your GPU	16	28.7 GB	Maximum	B59