Will It Run AI

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
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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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well23.4 tok/s4514 ms29K
CodingBTight fit23.4 tok/s8276 ms29K
Agentic CodingBRuns with offload (needs ~0.3 GB host RAM)21.8 tok/s12904 ms29K
ReasoningBTight fit23.4 tok/s9780 ms29K
RAGBRuns with offload (needs ~0.3 GB host RAM)21.8 tok/s16129 ms29K

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).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowB63
Q3_K_S
3
6.9 GB
LowB64
NVFP4
4
7.8 GB
MediumB65
Q4_K_M
4
8.5 GB
MediumB66
Q5_K_M
5
10.1 GB
HighB65
Q6_KBest for your GPU
6
11.5 GB
HighB65
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

Get started

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

Run

ollama run qwen2.5-coder:14b

Opciones de mejora

Hardware que ejecuta bien Qwen 2.5 Coder 14B

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 24GBSee all hardware for Qwen 2.5 Coder 14B
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