Can Gemma 4 E4B run on MacBook Pro M1 Pro 16GB?

YES — Runs Great

A80Great

○Estimated from fit model

Gemma 4 E4B needs ~8.8 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~22 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) — 8.8 GB, 21.7 tok/s, Runs well

8.8 GB required11.5 GB available

77% VRAM used

Fit status

Runs well

Decode

21.7 tok/s

TTFT

8920 ms

Safe context

50K

Memory

8.8 GB / 11.5 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on MacBook Pro M1 Pro 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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 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	21.7 tok/s	4866 ms	50K
Coding	A	Runs well	21.7 tok/s	8920 ms	50K
Agentic Coding	A	Tight fit	21.7 tok/s	12975 ms	50K
Reasoning	A	Runs well	21.7 tok/s	10542 ms	50K
RAG	A	Tight fit	21.7 tok/s	16219 ms	50K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A77
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A79
Q4_K_M	4	4.9 GB	Medium	A80
Q5_K_M	5	5.8 GB	High	A80
Q6_KBest for your GPU	6	6.6 GB	High	A79
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 E4B on your machine.

Run

ollama run gemma4:e4b

Your hardware

More models your MacBook Pro M1 Pro 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3.5 9B	9B	S	25.5 tok/s
👁 Alibaba Qwen 3 14B	14B	A	12.8 tok/s
👁 Mistral Ministral 3 14B	14B	B	12.7 tok/s
👁 NVIDIA Nemotron Nano 9B v2	9B	A	25.5 tok/s
👁 Tsinghua/Zhipu CodeGeeX 4 9B	9B	A	25.9 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Pro 16GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-m1-pro-16gb

⇱ Gemma 4 E4B on MacBook Pro M1 Pro 16GB? YES