Is unified memory on MacBook Air M3 24GB as fast as VRAM for Gemma 3 12B?

Not always. MacBook Air M3 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.

Can Gemma 3 12B run on MacBook Air M3 24GB?

Q: What should I upgrade first if Gemma 3 12B feels slow on MacBook Air M3 24GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Tight Fit

A76Great

Estimated from fit model

Gemma 3 12B needs ~15.7 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~7 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 15.7 GB, 7.4 tok/s, Tight fit

15.7 GB required17.3 GB available

91% VRAM used

Fit status

Tight fit

Decode

7.4 tok/s

TTFT

26190 ms

Safe context

21K

Memory

15.7 GB / 17.3 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsGemma 3 12B on MacBook Air M3 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: 7.4 tok/s decode · 26.2s TTFT (warm) · 19 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	7.4 tok/s	14285 ms	21K
Coding	A	Tight fit	7.4 tok/s	26190 ms	21K
Agentic Coding	B	Very compromised (needs ~1.2 GB host RAM)	5.7 tok/s	49545 ms	21K
Reasoning	A	Tight fit	7.4 tok/s	30951 ms	21K
RAG	B	Very compromised (needs ~1.2 GB host RAM)	5.7 tok/s	61931 ms

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A77
Q3_K_S	3	5.9 GB	Low	A79
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Your hardware

More models your MacBook Air M3 24GB can run

Model	Params	Grade	Decode	Capabilities
Magistral Small 2507	24B	B	3.8 tok/s
Devstral Small 2 24B Instruct	24B	B	3.8 tok/s

Frequently asked questions

Can MacBook Air M3 24GB run Gemma 3 12B?

Yes, MacBook Air M3 24GB can run Gemma 3 12B with a A grade (Tight fit). Expected decode speed: 7.4 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 12B?

The recommended quantization for Gemma 3 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 12B run at on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Gemma 3 12B achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26190ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on MacBook Air M3 24GB receives a A grade with 7.4 tok/s and 21K context.

What context window can Gemma 3 12B use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Gemma 3 12B can safely use up to 21K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 3 12B feels slow on MacBook Air M3 24GB?

See all results for MacBook Air M3 24GB See all hardware for Gemma 3 12B

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