Can Gemma 4 E4B run on MacBook Pro M1 Max 32GB?

YES — Runs Great

A77Great

Estimated from fit model

Gemma 4 E4B needs ~10.8 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 10.8 GB, 48.5 tok/s, Runs well

10.8 GB required23.0 GB available

47% VRAM used

Fit status

Runs well

Decode

48.5 tok/s

TTFT

3995 ms

Safe context

128K

Memory

10.8 GB / 23.0 GB

Memory breakdown

Weights4.9 GB

KV Cache1.3 GB

Runtime1.2 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsGemma 4 E4B on MacBook Pro M1 Max 32GB
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: 48.5 tok/s decode · 4.0s TTFT (warm) · 121 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	48.5 tok/s	2179 ms	128K
Coding	A	Runs well	48.5 tok/s	3995 ms	128K
Agentic Coding	A	Runs well	48.5 tok/s	5811 ms	128K
Reasoning	A	Runs well	48.5 tok/s	4721 ms	128K
RAG	A	Runs well	48.5 tok/s	7263 ms	128K

Quantization options

How Gemma 4 E4B (8B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A72
Q3_K_S	3	3.9 GB	Low	A73
NVFP4	4	4.5 GB	Medium	A73
Q4_K_M	4	4.9 GB	Medium	A73
Q5_K_M	5	5.8 GB	High	A74
Q6_K	6	6.6 GB	High	A74
Q8_0	8	8.6 GB	Very High	A75
F16Best for your GPU	16	16.4 GB	Maximum	A77

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 Max 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	29.4 tok/s
Qwen 3.5 27B	27B	A	13.1 tok/s
Qwen 3.6 27B	27B	S	14.5 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	30.9 tok/s
Qwen 3.5 9B	9B	S	43.1 tok/s

Frequently asked questions

Can MacBook Pro M1 Max 32GB run Gemma 4 E4B?

Yes, MacBook Pro M1 Max 32GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 48.5 tok/s.

How much VRAM does Gemma 4 E4B need?

Gemma 4 E4B (8B parameters) requires approximately 10.8 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 MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, Gemma 4 E4B achieves approximately 48.5 tokens per second decode speed with a time-to-first-token of 3995ms using Q4_K_M quantization.

Can MacBook Pro M1 Max 32GB run Gemma 4 E4B for coding?

For coding workloads, Gemma 4 E4B on MacBook Pro M1 Max 32GB receives a A grade with 48.5 tok/s and 128K context.

What context window can Gemma 4 E4B use on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, 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 MacBook Pro M1 Max 32GB as fast as VRAM for Gemma 4 E4B?

Not always. MacBook Pro M1 Max 32GB 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 MacBook Pro M1 Max 32GB See all hardware for Gemma 4 E4B

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