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URL: https://willitrunai.com/can-run/phi-3-medium-14b-on-m2-pro-32gb


Can Phi 3 Medium 14B run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

B62Good
Estimated from fit model

Phi 3 Medium 14B needs ~15.9 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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) — 15.9 GB, 17.6 tok/s, Runs well
15.9 GB required23.0 GB available
69% VRAM used

Fit status

Runs well

Decode

17.6 tok/s

TTFT

10986 ms

Safe context

53K

Memory

15.9 GB / 23.0 GB

Memory breakdown

Weights8.5 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsPhi 3 Medium 14B on MacBook Pro M2 Pro 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: 17.6 tok/s decode · 11.0s TTFT (warm) · 44 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 well16.4 tok/s6442 ms53K
CodingBRuns well16.4 tok/s11810 ms53K
Agentic CodingBTight fit16.4 tok/s17178 ms53K
ReasoningBRuns well16.4 tok/s13957 ms53K
RAGBTight fit16.4 tok/s21472 ms53K

Quantization options

How Phi 3 Medium 14B (14B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowB57
Q3_K_S
3
6.9 GB
LowB58
NVFP4
4

Get started

Copy-paste commands to run Phi 3 Medium 14B on your machine.

Run

ollama run phi3:medium

Upgrade options

Hardware that runs Phi 3 Medium 14B well

👁 Intel
Intel Arc Pro B60 24GBBest value
456 GB/s (+256)
B
Raises estimated decode speed by about 76%.31 tok/s decode

Raises estimated decode speed by about 76%.

~$599 MSRP

MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+210)
B
Raises estimated decode speed by about 85%.32.5 tok/s decode

Raises estimated decode speed by about 85%.

~$2,499 MSRP

Frequently asked questions

See all results for MacBook Pro M2 Pro 32GBSee all hardware for Phi 3 Medium 14B
7.8 GB
Medium
B59
Q4_K_M
4
8.5 GB
MediumB59
Q5_K_M
5
10.1 GB
HighB60
Q6_K
6
11.5 GB
HighB61
Q8_0Best for your GPU
8
15.0 GB
Very HighB61
F16
16
28.7 GB
MaximumF0

Not always. MacBook Pro M2 Pro 32GB 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.