Is unified memory on MacBook Pro M1 Max 32GB as fast as VRAM for Gemma 4 26B A4B?

Not always. MacBook Pro M1 Max 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 4 26B A4B run on MacBook Pro M1 Max 32GB?

Q: What should I upgrade first if Gemma 4 26B A4B feels slow on MacBook Pro M1 Max 32GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

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 ~33 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

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	34.0 tok/s	3104 ms	14K
Coding	S	Runs with offload	32.8 tok/s	5895 ms	14K
Agentic Coding	A	Very compromised	26.6 tok/s	10573 ms	14K
Reasoning	S	Runs with offload	32.8 tok/s	6966 ms	14K
RAG	A	Very compromised	26.6 tok/s	13217 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

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	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	29.9 tok/s
Qwen 3.5 27B	27B	S	13.3 tok/s

Frequently asked questions

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

Yes, MacBook Pro M1 Max 32GB can run Gemma 4 26B A4B with a S grade (Runs with offload). Expected decode speed: 32.8 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 23.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 26B A4B?

The recommended quantization for Gemma 4 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 26B A4B run at on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, Gemma 4 26B A4B achieves approximately 32.8 tokens per second decode speed with a time-to-first-token of 5895ms using Q4_K_M quantization.

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

For coding workloads, Gemma 4 26B A4B on MacBook Pro M1 Max 32GB receives a S grade with 32.8 tok/s and 14K context.

What context window can Gemma 4 26B A4B use on MacBook Pro M1 Max 32GB?

On MacBook Pro M1 Max 32GB, Gemma 4 26B A4B can safely use up to 14K 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 26B A4B feels slow on MacBook Pro M1 Max 32GB?

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

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