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⇱ Qwen 2.5 14B on MacBook Pro M1 Max 32GB? YES

Can Qwen 2.5 14B run on MacBook Pro M1 Max 32GB?

YES — Runs Great

A84Great
○Estimated from fit model

Qwen 2.5 14B needs ~15.8 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced
Share:

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) — 15.8 GB, 27.8 tok/s, Runs well
15.8 GB required23.0 GB available
69% VRAM used

Fit status

Runs well

Decode

27.8 tok/s

TTFT

6959 ms

Safe context

55K

Memory

15.8 GB / 23.0 GB

Memory breakdown

Weights8.5 GB
KV Cache2.9 GB
Runtime0.9 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsQwen 2.5 14B on MacBook Pro M1 Max 32GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step. Given information: - Speed = 60 mph (miles per hour) - Time = 2.5 hours Formula: Distance = Speed x Time Calculation: Distance = 60 mph x 2.5 h = 150 miles The train travels 150 miles. Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time. Breakdown: - First half: 1.25 hours at 60 mph = 75 miles - Stop: 20 minutes (no distance) - Second half: 1.25 hours at 60 mph = 75 miles Total distance: 150 miles (same) Total time: 2.5 h + 0.33 h = 2.83 hours Average speed: 150 / 2.83 = 53 mph The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 27.8 tok/s decode · 7.0s TTFT (warm) · 70 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
ChatARuns well27.8 tok/s3796 ms55K
CodingARuns well27.8 tok/s6959 ms55K
Agentic CodingARuns well27.8 tok/s10121 ms55K
ReasoningARuns well27.8 tok/s8224 ms55K
RAGARuns well27.8 tok/s12652 ms55K

Quantization options

How Qwen 2.5 14B (14B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowA77
Q3_K_S
3
6.9 GB
LowA78
NVFP4
4
7.8 GB
MediumA78
Q4_K_M
4
8.5 GB
MediumA79
Q5_K_M
5
10.1 GB
HighA80
Q6_K
6
11.5 GB
HighA81
Q8_0Best for your GPU
8
15.0 GB
Very HighA81
F16
16
28.7 GB
MaximumF0

Get started

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

Run

ollama run qwen2.5

Your hardware

More models your MacBook Pro M1 Max 32GB can run

ModelParamsGradeDecodeCapabilities
👁 Alibaba
Qwen3-Coder 30B A3B Instruct		30.5BA29.9 tok/s
👁 Alibaba
Qwen 3.5 27B		27BS13.3 tok/s
👁 Alibaba
Qwen 3.6 27B		27BS11 tok/s
👁 Alibaba
Qwen3-VL 30B A3B Instruct		30BS31.5 tok/s
👁 Alibaba
Qwen 3.5 35B A3B		35BA26 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Max 32GBSee all hardware for Qwen 2.5 14B