Is unified memory on MacBook Pro M4 Pro 24GB as fast as VRAM for Gemma 4 12B?

Not always. MacBook Pro M4 Pro 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 4 12B run on MacBook Pro M4 Pro 24GB?

Q: What should I upgrade first if Gemma 4 12B feels slow on MacBook Pro M4 Pro 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — With Offload

A81Great

Estimated — low-sample bucket· few comparable runs

Gemma 4 12B needs ~16.7 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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) — 16.7 GB, 20.1 tok/s, Runs with offload

16.7 GB required17.3 GB available

97% VRAM used

Fit status

Runs with offload

Decode

20.1 tok/s

TTFT

9627 ms

Safe context

18K

Memory

16.7 GB / 17.3 GB

Memory breakdown

Weights7.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsGemma 4 12B on MacBook Pro M4 Pro 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: 20.1 tok/s decode · 9.6s TTFT (warm) · 50 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	20.1 tok/s	5251 ms	18K
Coding	A	Runs with offload	21.8 tok/s	8896 ms	18K
Agentic Coding	F	Too heavy	13.8 tok/s	20349 ms	18K
Reasoning	A	Runs with offload	20.1 tok/s	11378 ms	18K
RAG	F	Too heavy	13.8 tok/s	25437 ms	18K

Inference speed

Gemma 4 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 12B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~168 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	168.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.9	Fits

Quantization options

How Gemma 4 12B (12B params) fits at each quantization level on MacBook Pro M4 Pro 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A79
Q3_K_S	3	5.9 GB	Low	A80
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 12B on your machine.

Run

lms load gemma-4-12B-it && lms server start

Your hardware

More models your MacBook Pro M4 Pro 24GB can run

Model	Params	Grade	Decode	Capabilities
Magistral Small 2507	24B	A	17.8 tok/s
Devstral Small 2 24B Instruct	24B	A	17.8 tok/s

Frequently asked questions

Can MacBook Pro M4 Pro 24GB run Gemma 4 12B?

Yes, MacBook Pro M4 Pro 24GB can run Gemma 4 12B with a A grade (Runs with offload). Expected decode speed: 21.8 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 16.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 12B?

The recommended quantization for Gemma 4 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 12B run at on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Gemma 4 12B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8896ms using Q4_K_M quantization.

Can MacBook Pro M4 Pro 24GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on MacBook Pro M4 Pro 24GB receives a A grade with 21.8 tok/s and 18K context.

What context window can Gemma 4 12B use on MacBook Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, Gemma 4 12B can safely use up to 18K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 12B feels slow on MacBook Pro M4 Pro 24GB?

See all results for MacBook Pro M4 Pro 24GB See all hardware for Gemma 4 12B

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