Can Gemma 4 E2B run on MacBook Pro M4 16GB?

YES — Runs Great

A72Great

▲Estimated — low-sample bucket· few comparable runs

Gemma 4 E2B needs ~6.3 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~21 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) — 6.3 GB, 21.1 tok/s, Runs well

6.3 GB required11.5 GB available

55% VRAM used

Fit status

Runs well

Decode

21.1 tok/s

TTFT

9194 ms

Safe context

128K

Memory

6.3 GB / 11.5 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on MacBook Pro M4 16GB
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: 21.1 tok/s decode · 9.2s TTFT (warm) · 53 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	21.1 tok/s	5015 ms	128K
Coding	A	Runs well	21.1 tok/s	9194 ms	128K
Agentic Coding	A	Runs well	21.1 tok/s	13373 ms	128K
Reasoning	A	Runs well	21.1 tok/s	10865 ms	128K
RAG	A	Runs well	21.1 tok/s	16716 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	A71
Q3_K_S	3	2.5 GB	Low	A72
NVFP4	4	2.9 GB	Medium	A72
Q4_K_M	4	3.1 GB	Medium	A73
Q5_K_M	5	3.7 GB	High	A74
Q6_K	6	4.2 GB	High	A74
Q8_0Best for your GPU	8	5.5 GB	Very High	A75
F16	16	10.5 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 E2B on your machine.

Run

ollama run gemma4:e2b

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 4 E2B

URL: https://willitrunai.com/can-run/gemma-4-e2b-on-m4-16gb