Can Gemma 4 E2B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

B68Good

○Estimated from fit model

Gemma 4 E2B needs ~18.4 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~71 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) — 18.4 GB, 71.4 tok/s, Runs well

18.4 GB required92.2 GB available

20% VRAM used

Fit status

Runs well

Decode

71.4 tok/s

TTFT

2711 ms

Safe context

128K

Memory

18.4 GB / 92.2 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on Mac Studio M2 Ultra 128GB
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: 71.4 tok/s decode · 2.7s TTFT (warm) · 179 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	B	Runs well	71.4 tok/s	1479 ms	128K
Coding	B	Runs well	71.4 tok/s	2711 ms	128K
Agentic Coding	B	Runs well	71.4 tok/s	3944 ms	128K
Reasoning	B	Runs well	71.4 tok/s	3204 ms	128K
RAG	B	Runs well	71.4 tok/s	4930 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B62
Q3_K_S	3	2.5 GB	Low	B62
NVFP4	4	2.9 GB	Medium	B62
Q4_K_M	4	3.1 GB	Medium	B62
Q5_K_M	5	3.7 GB	High	B62
Q6_K	6	4.2 GB	High	B62
Q8_0	8	5.5 GB	Very High	B62
F16Best for your GPU	16	10.5 GB	Maximum	B62

Get started

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

Run

ollama run gemma4:e2b

Frequently asked questions

See all results for Mac Studio M2 Ultra 128GB See all hardware for Gemma 4 E2B

URL: https://willitrunai.com/can-run/gemma-4-e2b-on-m2-ultra-128gb

⇱ Gemma 4 E2B on Mac Studio M2 Ultra 128GB? YES