Is unified memory on MacBook Pro M1 Pro 16GB as fast as VRAM for Gemma 4 31B?

Not always. MacBook Pro M1 Pro 16GB 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 31B run on MacBook Pro M1 Pro 16GB?

Q: What should I upgrade first if Gemma 4 31B feels slow on MacBook Pro M1 Pro 16GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 4 31B needs ~36.0 GB but MacBook Pro M1 Pro 16GB only has 11.5 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 36.0 GB, exceeds 11.5 GB available

36.0 GB required11.5 GB available

313% VRAM needed

24.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.5 tok/s

TTFT

77883 ms

Safe context

Memory

36.0 GB / 11.5 GB

Offload

70%

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 31B on MacBook Pro M1 Pro 16GB
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: 2.5 tok/s decode · 77.9s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 36.0 GB, but this setup only exposes 11.5 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.5 tok/s	42481 ms	4K
Coding	F	Too heavy	2.5 tok/s	77883 ms	4K
Agentic Coding	F	Too heavy	2.5 tok/s	113284 ms	4K
Reasoning	F	Too heavy	2.5 tok/s	92043 ms	4K
RAG	F	Too heavy	2.5 tok/s	141605 ms	4K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	F0
Q3_K_S	3	15.0 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Gemma 4 31B well

Mac mini M4 64GBBudget pick

64 GB Unified (+48)

Makes the model fit on the accelerator instead of staying completely out of reach.6.6 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.

~$1,099 MSRP

MacBook Pro M4 Pro 64GBBest value

64 GB Unified (+48)273 GB/s (+73)

Makes the model fit on the accelerator instead of staying completely out of reach.16.1 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 M4 Max 96GBApple upgrade

96 GB Unified (+80)546 GB/s (+346)

Makes the model fit on the accelerator instead of staying completely out of reach.25.5 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 M1 Pro 16GB run Gemma 4 31B?

No, Gemma 4 31B requires more memory than MacBook Pro M1 Pro 16GB provides.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 36.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, Gemma 4 31B achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 77883ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 16GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on MacBook Pro M1 Pro 16GB receives a F grade with 2.5 tok/s and 4K context.

What context window can Gemma 4 31B use on MacBook Pro M1 Pro 16GB?

On MacBook Pro M1 Pro 16GB, Gemma 4 31B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 31B feels slow on MacBook Pro M1 Pro 16GB?

See all results for MacBook Pro M1 Pro 16GB See all hardware for Gemma 4 31B

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