Can Qwen 2.5 Coder 14B run on MacBook Pro M4 Pro 24GB?

YES — Tight Fit

B64Good

Estimated — low-sample bucket· few comparable runs

Qwen 2.5 Coder 14B needs ~15.0 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 15.0 GB, 23.4 tok/s, Tight fit

15.0 GB required17.3 GB available

87% VRAM used

Fit status

Tight fit

Decode

23.4 tok/s

TTFT

8276 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 Pro M4 Pro 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: 23.4 tok/s decode · 8.3s TTFT (warm) · 59 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	23.4 tok/s	4514 ms	29K
Coding	B	Tight fit	23.4 tok/s	8276 ms	29K
Agentic Coding	B	Runs with offload (needs ~0.3 GB host RAM)	21.8 tok/s	12904 ms	29K
Reasoning	B	Tight fit	23.4 tok/s	9780 ms	29K
RAG	B	Runs with offload (needs ~0.3 GB host RAM)	21.8 tok/s	16129 ms	29K

Inference speed

Qwen 2.5 Coder 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	151.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.4	Fits
RTX 3090 24GB	24 GB	Q4_K_M	82.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	81.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	29.1	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	17.0	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	6.3	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on MacBook Pro M4 Pro 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	7.8 GB	Medium	B65
Q4_K_M	4	8.5 GB	Medium	B66
Q5_K_M	5	10.1 GB	High	B65
Q6_KBest for your GPU	6	11.5 GB	High	B65
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

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

Run

ollama run qwen2.5-coder:14b

Upgrade-Optionen

Hardware, die Qwen 2.5 Coder 14B gut ausführt

MacBook Pro M2 Max 32GBGünstige Wahl

32 GB Unified (+8)400 GB/s (+127)

Raises estimated decode speed by about 25%.29.3 tok/s Dekodierung

Raises estimated decode speed by about 25%.

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

ca. $1,999 MSRP

MacBook Pro M2 Pro 32GBBestes Preis-Leistungs-Verhältnis

32 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.17.7 tok/s Dekodierung

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

ca. $1,999 MSRP

MacBook Pro M1 Pro 32GBApple-Upgrade

32 GB Unified (+8)

Adds memory headroom for longer context windows and future model growth.16.4 tok/s Dekodierung

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

ca. $1,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run Qwen 2.5 Coder 14B?

Yes, MacBook Pro M4 Pro 24GB can run Qwen 2.5 Coder 14B with a B grade (Tight fit). Expected decode speed: 23.4 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 Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Qwen 2.5 Coder 14B achieves approximately 23.4 tokens per second decode speed with a time-to-first-token of 8276ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on MacBook Pro M4 Pro 24GB receives a B grade with 23.4 tok/s and 29K context.

What context window can Qwen 2.5 Coder 14B use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 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 Pro M4 Pro 24GB as fast as VRAM for Qwen 2.5 Coder 14B?

Not always. MacBook Pro M4 Pro 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 MacBook Pro M4 Pro 24GB See all hardware for Qwen 2.5 Coder 14B

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