Can Gemma 4 26B A4B run on MacBook Pro M1 Max 32GB?

YES — With Offload

S86Excellent

○Estimated from fit model

Gemma 4 26B A4B needs ~23.4 GB VRAM. MacBook Pro M1 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 23.4 GB, 34.5 tok/s, Runs with offload (needs ~0.2 GB host RAM)

23.4 GB required23.0 GB available

102% VRAM needed

0.4 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

34.5 tok/s

TTFT

5614 ms

Safe context

14K

Memory

23.4 GB / 23.0 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B on MacBook Pro M1 Max 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: 34.5 tok/s decode · 5.6s TTFT (warm) · 86 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	35.7 tok/s	2956 ms	14K
Coding	S	Runs with offload (needs ~0.2 GB host RAM)	34.5 tok/s	5614 ms	14K
Agentic Coding	A	Very compromised (needs ~2.3 GB host RAM)	28.0 tok/s	10070 ms	14K
Reasoning	S	Runs with offload (needs ~0.2 GB host RAM)	34.5 tok/s	6635 ms	14K
RAG	A	Very compromised (needs ~2.3 GB host RAM)	28.0 tok/s	12587 ms	14K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on MacBook Pro M1 Max 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A84
Q3_K_S	3	12.3 GB	Low	S85
NVFP4	4	14.1 GB	Medium	S85
Q4_K_M	4	15.4 GB	Medium	A85
Q5_K_MBest for your GPU	5	18.1 GB	High	A84
Q6_K	6	20.7 GB	High	F0
Q8_0	8	27.0 GB	Very High	F0
F16	16	51.7 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 26B A4B on your machine.

Run

ollama run gemma4:26b

Your hardware

More models your MacBook Pro M1 Max 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	29.9 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	13.3 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	11 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	31.5 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	26 tok/s

Frequently asked questions

See all results for MacBook Pro M1 Max 32GB See all hardware for Gemma 4 26B A4B

URL: https://willitrunai.com/can-run/gemma-4-26b-a4b-on-m1-max-32gb

⇱ Gemma 4 26B A4B on MacBook Pro M1 Max 32GB? YES