Can Gemma 4 E4B run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

A75Great

Estimated from fit model

Gemma 4 E4B needs ~17.4 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~93 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) — 17.4 GB, 93.0 tok/s, Runs well

17.4 GB required69.1 GB available

25% VRAM used

Fit status

Runs well

Decode

93.0 tok/s

TTFT

2082 ms

Safe context

128K

Memory

17.4 GB / 69.1 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on Mac Studio M3 Ultra 96GB
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: 93.0 tok/s decode · 2.1s TTFT (warm) · 232 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	93.0 tok/s	1136 ms	128K
Coding	A	Runs well	93.0 tok/s	2082 ms	128K
Agentic Coding	A	Runs well	93.0 tok/s	3029 ms	128K
Reasoning	A	Runs well	86.5 tok/s	2645 ms	128K
RAG	A	Runs well	93.0 tok/s	3786 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B67
Q3_K_S	3	3.9 GB	Low	B68
NVFP4	4	4.5 GB	Medium	B68
Q4_K_M	4	4.9 GB	Medium	B68
Q5_K_M	5	5.8 GB	High	B68
Q6_K	6	6.6 GB	High	B68
Q8_0	8	8.6 GB	Very High	B68
F16Best for your GPU	16	16.4 GB	Maximum	B69

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 M3 Ultra 96GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	84.2 tok/s
Qwen 3.5 27B	27B	S	36.5 tok/s
Qwen 3.6 27B	27B	S	27.8 tok/s
Qwen 3.6 35B A3B	35B	S	70.8 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	87.1 tok/s

Frequently asked questions

Can Mac Studio M3 Ultra 96GB run Gemma 4 E4B?

Yes, Mac Studio M3 Ultra 96GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 93.0 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 17.4 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 Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, Gemma 4 E4B achieves approximately 93.0 tokens per second decode speed with a time-to-first-token of 2082ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 96GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on Mac Studio M3 Ultra 96GB receives a A grade with 93.0 tok/s and 128K context.

What context window can Gemma 4 E4B use on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, 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 Studio M3 Ultra 96GB as fast as VRAM for Gemma 4 E4B?

Not always. Mac Studio M3 Ultra 96GB 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 Studio M3 Ultra 96GB See all hardware for Gemma 4 E4B

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