Can Gemma 3 4B run on MacBook Pro M4 16GB?

YES — Runs Great

A72Great

▲Estimated — low-sample bucket· few comparable runs

Gemma 3 4B needs ~7.1 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 7.1 GB, 25.9 tok/s, Runs well

7.1 GB required11.5 GB available

62% VRAM used

Fit status

Runs well

Decode

25.9 tok/s

TTFT

7468 ms

Safe context

50K

Memory

7.1 GB / 11.5 GB

Memory breakdown

Weights2.4 GB

KV Cache2.1 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGemma 3 4B on MacBook Pro M4 16GB
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: 25.9 tok/s decode · 7.5s TTFT (warm) · 65 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	A	Runs well	25.9 tok/s	4074 ms	50K
Coding	A	Runs well	25.9 tok/s	7468 ms	50K
Agentic Coding	A	Runs well	25.9 tok/s	10863 ms	50K
Reasoning	A	Runs well	25.9 tok/s	8826 ms	50K
RAG	A	Runs well	25.9 tok/s	13579 ms	50K

Quantization options

How Gemma 3 4B (4B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	B69
Q3_K_S	3	2.0 GB	Low	B70
NVFP4	4	2.2 GB	Medium	B70
Q4_K_M	4	2.4 GB	Medium	A70
Q5_K_M	5	2.9 GB	High	A71
Q6_K	6	3.3 GB	High	A71
Q8_0	8	4.3 GB	Very High	A73
F16Best for your GPU	16	8.2 GB	Maximum	A73

Get started

Copy-paste commands to run Gemma 3 4B on your machine.

Run

ollama run gemma3:4b

Your hardware

More models your MacBook Pro M4 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	15.6 tok/s
👁 Alibaba Qwen 3 14B	14B	A	7.5 tok/s
👁 Alibaba Qwen 3 8B	8B	S	17.5 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	18.9 tok/s
👁 Mistral Ministral 3 14B	14B	B	7.4 tok/s

Frequently asked questions

See all results for MacBook Pro M4 16GB See all hardware for Gemma 3 4B

URL: https://willitrunai.com/can-run/gemma-3-4b-on-m4-16gb