Is unified memory on MacBook Pro M3 Pro 18GB as fast as VRAM for Gemma 3 12B?

Not always. MacBook Pro M3 Pro 18GB 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 Pro M3 Pro 18GB?

Q: What should I upgrade first if Gemma 3 12B feels slow on MacBook Pro M3 Pro 18GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

B66Good

Estimated from fit model

Gemma 3 12B needs ~15.0 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 15.0 GB, 9.5 tok/s, Very compromised (needs ~1 GB host RAM)

15.0 GB required13.0 GB available

115% VRAM needed

2.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

9.5 tok/s

TTFT

20481 ms

Safe context

Memory

15.0 GB / 13.0 GB

Offload

10%

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsGemma 3 12B on MacBook Pro M3 Pro 18GB
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: 9.5 tok/s decode · 20.5s TTFT (warm) · 24 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	11.9 tok/s	8871 ms	9K
Coding	B	Very compromised (needs ~1 GB host RAM)	9.5 tok/s	20481 ms	9K
Agentic Coding	F	Too heavy	6.8 tok/s	41611 ms	9K
Reasoning	B	Very compromised (needs ~1 GB host RAM)	9.5 tok/s	24204 ms	9K
RAG	F	Too heavy	6.8 tok/s	52014 ms

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A80
Q3_K_S	3	5.9 GB	Low	A82
NVFP4	4

Get started

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

Run

ollama run gemma3:12b

Upgrade options

Hardware that runs Gemma 3 12B well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+14)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+14)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,099 MSRP

MacBook Air M4 24GBApple upgrade

24 GB Unified (+6)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,099 MSRP

RTX A4500 20GBBiggest leap

20 GB VRAM (+2)640 GB/s (+490)

Removes host-memory offload, which is usually the single biggest latency and throughput win.54.3 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 472%.

~$2,000 MSRP

Frequently asked questions

Can MacBook Pro M3 Pro 18GB run Gemma 3 12B?

Yes, MacBook Pro M3 Pro 18GB can run Gemma 3 12B with a B grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 9.5 tok/s.

How much VRAM does Gemma 3 12B need?

Gemma 3 12B (12B parameters) requires approximately 15.0 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 Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Gemma 3 12B achieves approximately 9.5 tokens per second decode speed with a time-to-first-token of 20481ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 18GB run Gemma 3 12B for coding?

For coding workloads, Gemma 3 12B on MacBook Pro M3 Pro 18GB receives a B grade with 9.5 tok/s and 9K context.

What context window can Gemma 3 12B use on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Gemma 3 12B can safely use up to 9K 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 Pro M3 Pro 18GB?

See all results for MacBook Pro M3 Pro 18GB See all hardware for Gemma 3 12B

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