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URL: https://willitrunai.com/can-run/phi-3-medium-14b-on-m2-24gb


Can Phi 3 Medium 14B run on Mac mini M2 24GB?

YES — Tight Fit

B57Good
Estimated from fit model

Phi 3 Medium 14B needs ~15.1 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 15.1 GB, 8.2 tok/s, Tight fit
15.1 GB required17.3 GB available
87% VRAM used

Fit status

Tight fit

Decode

8.2 tok/s

TTFT

23661 ms

Safe context

28K

Memory

15.1 GB / 17.3 GB

Memory breakdown

Weights8.5 GB
KV Cache3.1 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

See how fast it feelsPhi 3 Medium 14B on Mac mini M2 24GB
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: 8.2 tok/s decode · 23.7s TTFT (warm) · 21 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well7.6 tok/s13874 ms28K
CodingBTight fit7.6 tok/s25436 ms28K
Agentic CodingBRuns with offload6.9 tok/s40518 ms28K
ReasoningBTight fit7.6 tok/s30061 ms28K
RAGBRuns with offload6.9 tok/s50648 ms28K

Quantization options

How Phi 3 Medium 14B (14B params) fits at each quantization level on Mac mini M2 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowB60
Q3_K_S
3
6.9 GB
LowB61
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

MacBook Pro M4 32GBBudget pick
32 GB Unified (+8)120 GB/s (+20)
B
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)
B
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

👁 NVIDIA
RTX A4500 20GBBiggest leap
640 GB/s (+540)
B
Raises estimated decode speed by about 666%.62.8 tok/s decode

Raises estimated decode speed by about 666%.

~$2,000 MSRP

Mac Studio M2 Ultra 64GBApple upgrade
64 GB Unified (+40)800 GB/s (+700)
B
Raises estimated decode speed by about 612%.58.4 tok/s decode

Raises estimated decode speed by about 612%.

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

~$3,999 MSRP

Frequently asked questions

See all results for Mac mini M2 24GBSee all hardware for Phi 3 Medium 14B
7.8 GB
Medium
B62
Q4_K_M
4
8.5 GB
MediumB62
Q5_K_M
5
10.1 GB
HighB62
Q6_KBest for your GPU
6
11.5 GB
HighB62
Q8_0
8
15.0 GB
Very HighF0
F16
16
28.7 GB
MaximumF0

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.