Can Gemma 4 31B run on MacBook Pro M3 Max 64GB?

YES — Tight Fit

A84Great

○Estimated from fit model

Gemma 4 31B needs ~41.2 GB VRAM. MacBook Pro M3 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 41.2 GB, 10.2 tok/s, Tight fit

41.2 GB required46.1 GB available

89% VRAM used

Fit status

Tight fit

Decode

10.2 tok/s

TTFT

18984 ms

Safe context

21K

Memory

41.2 GB / 46.1 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGemma 4 31B on MacBook Pro M3 Max 64GB
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: 10.2 tok/s decode · 19.0s TTFT (warm) · 26 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	9.7 tok/s	10873 ms	21K
Coding	A	Tight fit	10.2 tok/s	18984 ms	21K
Agentic Coding	F	Too heavy	7.7 tok/s	36703 ms	21K
Reasoning	A	Tight fit	10.2 tok/s	22435 ms	21K
RAG	F	Too heavy	7.7 tok/s	45879 ms	21K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on MacBook Pro M3 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A81
Q3_K_S	3	15.0 GB	Low	A82
NVFP4	4

Get started

Copy-paste commands to run Gemma 4 31B on your machine.

Run

ollama run gemma4:31b

Your hardware

More models your MacBook Pro M3 Max 64GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	33.5 tok/s
👁 Alibaba

Frequently asked questions

See all results for MacBook Pro M3 Max 64GB See all hardware for Gemma 4 31B

URL: https://willitrunai.com/can-run/gemma-4-31b-on-m3-max-64gb

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