Can Gemma 3 27B run on MacBook Pro M4 Max 96GB?

YES — Runs Great

A82Great

○Estimated from fit model

Gemma 3 27B needs ~39.0 GB VRAM. MacBook Pro M4 Max 96GB has 69.1 GB. With Q4_K_M quantization, expect ~27 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 39.0 GB, 26.6 tok/s, Runs well

39.0 GB required69.1 GB available

56% VRAM used

Fit status

Runs well

Decode

26.6 tok/s

TTFT

7278 ms

Safe context

59K

Memory

39.0 GB / 69.1 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsGemma 3 27B on MacBook Pro M4 Max 96GB
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: 26.6 tok/s decode · 7.3s TTFT (warm) · 67 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	A	Runs well	26.6 tok/s	3970 ms	59K
Coding	A	Runs well	26.6 tok/s	7278 ms	59K
Agentic Coding	S	Runs well	26.6 tok/s	10587 ms	59K
Reasoning	A	Runs well	26.6 tok/s	8602 ms	59K
RAG	S	Runs well	26.6 tok/s	13233 ms	59K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on MacBook Pro M4 Max 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A74
Q3_K_S	3	13.2 GB	Low	A74
NVFP4	4	15.1 GB	Medium	A74
Q4_K_M	4	16.5 GB	Medium	A75
Q5_K_M	5	19.4 GB	High	A75
Q6_K	6	22.1 GB	High	A76
Q8_0	8	28.9 GB	Very High	A77
F16Best for your GPU	16	55.4 GB	Maximum	A80

Get started

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

Run

ollama run gemma3

Your hardware

More models your MacBook Pro M4 Max 96GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	52 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	43.7 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	53.8 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	47.5 tok/s
👁 Alibaba Qwen 3 32B	32B	S	33.5 tok/s

Frequently asked questions

See all results for MacBook Pro M4 Max 96GB See all hardware for Gemma 3 27B

URL: https://willitrunai.com/can-run/gemma-3-27b-on-m4-max-96gb

⇱ Gemma 3 27B on MacBook Pro M4 Max 96GB? YES