Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Gemma 3 27B?

Not always. MacBook Pro M2 Pro 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.

Can Gemma 3 27B run on MacBook Pro M2 Pro 32GB?

Q: What should I upgrade first if Gemma 3 27B feels slow on MacBook Pro M2 Pro 32GB?

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.

YES — With Q2_K

B68Good

Estimated from fit model

Gemma 3 27B needs ~26.1 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q2_K quantization, expect ~7 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.

Gemma 3 27B at Q4_K_M needs 32.1 GB — too much for MacBook Pro M2 Pro 32GB (23.0 GB). Runs at Q2_K (26.1 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 32.1 GB, exceeds 23.0 GB available

32.1 GB required23.0 GB available

140% VRAM needed

9.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.3 tok/s

TTFT

44896 ms

Safe context

Memory

32.1 GB / 23.0 GB

Offload

30%

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 3 27B on MacBook Pro M2 Pro 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: 4.3 tok/s decode · 44.9s TTFT (warm) · 11 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 1.2 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	B	Very compromised	5.2 tok/s	20334 ms	4K
Coding	F	Too heavy	4.1 tok/s	47141 ms	4K
Agentic Coding	F	Too heavy	2.9 tok/s	95968 ms	4K
Reasoning	F	Too heavy	4.1 tok/s	55712 ms	4K
RAG	F	Too heavy	2.9 tok/s	119960 ms	4K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A82
Q3_K_S	3	13.2 GB	Low	A83
NVFP4	4

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

Upgrade options

Hardware that runs Gemma 3 27B well

Mac mini M4 64GBBudget pick

64 GB Unified (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.6.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+32)273 GB/s (+73)

Makes the model fit on the accelerator instead of staying completely out of reach.16.8 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,599 MSRP

MacBook Pro M3 Max 64GBApple upgrade

64 GB Unified (+32)400 GB/s (+200)

Makes the model fit on the accelerator instead of staying completely out of reach.11.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Gemma 3 27B?

Yes, MacBook Pro M2 Pro 32GB can run Gemma 3 27B at Q2_K quantization (Very compromised (needs ~1.2 GB host RAM)). The recommended Q4_K_M requires 32.1 GB which exceeds available memory, but at Q2_K it needs only 26.1 GB. Expected decode speed: 7.4 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 32.1 GB at Q4_K_M quantization. On MacBook Pro M2 Pro 32GB, it fits at Q2_K using 26.1 GB.

What is the best quantization for Gemma 3 27B?

The recommended quantization is Q4_K_M, but on MacBook Pro M2 Pro 32GB the best fitting quantization is Q2_K, which uses 26.1 GB.

What speed will Gemma 3 27B run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Gemma 3 27B achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26276ms using Q2_K quantization.

Can MacBook Pro M2 Pro 32GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on MacBook Pro M2 Pro 32GB receives a F grade with 4.1 tok/s and 4K context.

What context window can Gemma 3 27B use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Gemma 3 27B can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Pro 32GB See all hardware for Gemma 3 27B

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