Can Gemma 4 E4B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A75Great

○Estimated from fit model

Gemma 4 E4B needs ~14.0 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~78 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 14.0 GB, 77.5 tok/s, Runs well

14.0 GB required46.1 GB available

30% VRAM used

Fit status

Runs well

Decode

77.5 tok/s

TTFT

2499 ms

Safe context

128K

Memory

14.0 GB / 46.1 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on Mac Studio M2 Ultra 64GB
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: 77.5 tok/s decode · 2.5s TTFT (warm) · 194 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	77.5 tok/s	1363 ms	128K
Coding	A	Runs well	77.5 tok/s	2499 ms	128K
Agentic Coding	A	Runs well	77.5 tok/s	3635 ms	128K
Reasoning	A	Runs well	77.5 tok/s	2954 ms	128K
RAG	A	Runs well	77.5 tok/s	4544 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B69
Q3_K_S	3	3.9 GB	Low	B69
NVFP4	4	4.5 GB	Medium	B69
Q4_K_M	4	4.9 GB	Medium	B69
Q5_K_M	5	5.8 GB	High	B69
Q6_K	6	6.6 GB	High	B70
Q8_0	8	8.6 GB	Very High	A70
F16Best for your GPU	16	16.4 GB	Maximum	A72

Get started

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

Run

ollama run gemma4:e4b

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	30.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	23.1 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	59 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	72.6 tok/s

Frequently asked questions

See all results for Mac Studio M2 Ultra 64GB See all hardware for Gemma 4 E4B

URL: https://willitrunai.com/can-run/gemma-4-e4b-on-m2-ultra-64gb

⇱ Gemma 4 E4B on Mac Studio M2 Ultra 64GB? YES