Can Gemma 4 E2B run on MacBook Air M4 24GB?

Q: Is unified memory on MacBook Air M4 24GB as fast as VRAM for Gemma 4 E2B?

Not always. MacBook Air M4 24GB 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.

YES — Runs Great

B69Good

Estimated — low-sample bucket· few comparable runs

Gemma 4 E2B needs ~7.1 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~21 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) — 7.1 GB, 21.1 tok/s, Runs well

7.1 GB required17.3 GB available

41% VRAM used

Fit status

Runs well

Decode

21.1 tok/s

TTFT

9194 ms

Safe context

128K

Memory

7.1 GB / 17.3 GB

Memory breakdown

Weights3.1 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsGemma 4 E2B on MacBook Air M4 24GB
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: 21.1 tok/s decode · 9.2s TTFT (warm) · 53 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	21.1 tok/s	5015 ms	128K
Coding	B	Runs well	21.1 tok/s	9194 ms	128K
Agentic Coding	B	Runs well	21.1 tok/s	13373 ms	128K
Reasoning	B	Runs well	21.1 tok/s	10865 ms	128K
RAG	B	Runs well	21.1 tok/s	16716 ms	128K

Quantization options

How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.0 GB	Low	B69
Q3_K_S	3	2.5 GB	Low	B69
NVFP4	4

Get started

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

Run

ollama run gemma4:e2b

Upgrade options

Hardware that runs Gemma 4 E2B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+280)

Raises estimated decode speed by about 191%.61.5 tok/s decode

Raises estimated decode speed by about 191%.

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

~$1,999 MSRP

MacBook Pro M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+80)

Raises estimated decode speed by about 76%.37.1 tok/s decode

Raises estimated decode speed by about 76%.

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

~$1,999 MSRP

MacBook Pro M1 Pro 32GBApple upgrade

32 GB Unified (+8)200 GB/s (+80)

Raises estimated decode speed by about 63%.34.4 tok/s decode

Raises estimated decode speed by about 63%.

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

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M4 24GB run Gemma 4 E2B?

Yes, MacBook Air M4 24GB can run Gemma 4 E2B with a B grade (Runs well). Expected decode speed: 21.1 tok/s.

How much VRAM does Gemma 4 E2B need?

Gemma 4 E2B (5.099999904632568B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 E2B?

The recommended quantization for Gemma 4 E2B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 E2B run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Gemma 4 E2B achieves approximately 21.1 tokens per second decode speed with a time-to-first-token of 9194ms using Q4_K_M quantization.

Can MacBook Air M4 24GB run Gemma 4 E2B for coding?

For coding workloads, Gemma 4 E2B on MacBook Air M4 24GB receives a B grade with 21.1 tok/s and 128K context.

What context window can Gemma 4 E2B use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, Gemma 4 E2B 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 Air M4 24GB as fast as VRAM for Gemma 4 E2B?

See all results for MacBook Air M4 24GB See all hardware for Gemma 4 E2B

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