Can Gemma 4 E4B run on Mac mini M4 64GB?

YES — Runs Great

B70Good

Estimated — low-sample bucket· few comparable runs

Gemma 4 E4B needs ~14.0 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~13 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) — 14.0 GB, 13.3 tok/s, Runs well

14.0 GB required46.1 GB available

30% VRAM used

Fit status

Runs well

Decode

13.3 tok/s

TTFT

14589 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 mini M4 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: 13.3 tok/s decode · 14.6s TTFT (warm) · 33 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	13.3 tok/s	7958 ms	128K
Coding	B	Runs well	13.3 tok/s	14589 ms	128K
Agentic Coding	A	Runs well	13.3 tok/s	21220 ms	128K
Reasoning	B	Runs well	13.3 tok/s	17242 ms	128K
RAG	A	Runs well	13.3 tok/s	26526 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Mac mini M4 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

Upgrade options

Hardware that runs Gemma 4 E4B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 371%.62.6 tok/s decode

Raises estimated decode speed by about 371%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+64)400 GB/s (+280)

Raises estimated decode speed by about 202%.40.1 tok/s decode

Raises estimated decode speed by about 202%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBApple upgrade

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 599%.93 tok/s decode

Raises estimated decode speed by about 599%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Gemma 4 E4B?

Yes, Mac mini M4 64GB can run Gemma 4 E4B with a B grade (Runs well). Expected decode speed: 13.3 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E4B?

The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E4B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Gemma 4 E4B achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14589ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on Mac mini M4 64GB receives a B grade with 13.3 tok/s and 128K context.

What context window can Gemma 4 E4B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Gemma 4 E4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Gemma 4 E4B?

Not always. Mac mini M4 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac mini M4 64GB See all hardware for Gemma 4 E4B

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<iframe src="https://willitrunai.com/embed/gemma-4-e4b-on-m4-mini-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

13.3 tok/s

7958 ms

128K

Coding

Runs well

13.3 tok/s

14589 ms

128K

Agentic Coding

Runs well

13.3 tok/s

21220 ms

128K

Reasoning

Runs well

13.3 tok/s

17242 ms

128K

RAG

Runs well

13.3 tok/s

26526 ms

128K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

B69

Q3_K_S

3.9 GB

Low

B69

NVFP4

4.5 GB

Medium

B69

Q4_K_M

4.9 GB

Medium

B69

Q5_K_M

5.8 GB

High

B69

Q6_K

6.6 GB

High

B70

Q8_0

8.6 GB

Very High

A70

F16Best for your GPU

16.4 GB

Maximum

A72

Frequently asked questions

Can Mac mini M4 64GB run Gemma 4 E4B?

Yes, Mac mini M4 64GB can run Gemma 4 E4B with a B grade (Runs well). Expected decode speed: 13.3 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E4B?

The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E4B run at on Mac mini M4 64GB?

On Mac mini M4 64GB, Gemma 4 E4B achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14589ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on Mac mini M4 64GB receives a B grade with 13.3 tok/s and 128K context.

What context window can Gemma 4 E4B use on Mac mini M4 64GB?

On Mac mini M4 64GB, Gemma 4 E4B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Gemma 4 E4B?