Can Qwen 2.5 Coder 14B run on MacBook Air M3 24GB?

Q: Is unified memory on MacBook Air M3 24GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. MacBook Air M3 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.

YES — Tight Fit

B61Good

Estimated from fit model

Qwen 2.5 Coder 14B needs ~15.0 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 15.0 GB, 8.6 tok/s, Tight fit

15.0 GB required17.3 GB available

87% VRAM used

Fit status

Tight fit

Decode

8.6 tok/s

TTFT

22513 ms

Safe context

29K

Memory

15.0 GB / 17.3 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on MacBook Air M3 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: 8.6 tok/s decode · 22.5s TTFT (warm) · 22 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	8.6 tok/s	12280 ms	29K
Coding	B	Tight fit	8.6 tok/s	22513 ms	29K
Agentic Coding	B	Runs with offload (needs ~0.3 GB host RAM)	8.0 tok/s	35102 ms	29K
Reasoning	B	Tight fit	8.6 tok/s	26606 ms	29K
RAG	B	Runs with offload (needs ~0.3 GB host RAM)	8.0 tok/s	43878 ms

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B63
Q3_K_S	3	6.9 GB	Low	B64
NVFP4	4

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

MacBook Pro M4 32GBBudget pick

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.9.6 tok/s decode

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

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+8)120 GB/s (+20)

Adds memory headroom for longer context windows and future model growth.9.6 tok/s decode

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

~$1,099 MSRP

MacBook Pro M4 Pro 64GBApple upgrade

64 GB Unified (+40)273 GB/s (+173)

Raises estimated decode speed by about 172%.23.4 tok/s decode

Raises estimated decode speed by about 172%.

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

~$1,599 MSRP

RTX A4500 20GBBiggest leap

640 GB/s (+540)

Raises estimated decode speed by about 634%.63.1 tok/s decode

Raises estimated decode speed by about 634%.

~$2,000 MSRP

Frequently asked questions

Can MacBook Air M3 24GB run Qwen 2.5 Coder 14B?

Yes, MacBook Air M3 24GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 8.6 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 15.0 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 Air M3 24GB?

On MacBook Air M3 24GB, Qwen 2.5 Coder 14B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22513ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on MacBook Air M3 24GB receives a B grade with 8.6 tok/s and 29K context.

What context window can Qwen 2.5 Coder 14B use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Qwen 2.5 Coder 14B can safely use up to 29K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M3 24GB as fast as VRAM for Qwen 2.5 Coder 14B?

See all results for MacBook Air M3 24GB 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-m3-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

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