Can Gemma 4 26B A4B run on MacBook Pro M4 Max 64GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Gemma 4 26B A4B needs ~26.8 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~56 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) — 26.8 GB, 55.9 tok/s, Runs well

26.8 GB required46.1 GB available

58% VRAM used

Fit status

Runs well

Decode

55.9 tok/s

TTFT

3466 ms

Safe context

100K

Memory

26.8 GB / 46.1 GB

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGemma 4 26B A4B on MacBook Pro M4 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: 55.9 tok/s decode · 3.5s TTFT (warm) · 140 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	55.9 tok/s	1891 ms	100K
Coding	S	Runs well	55.9 tok/s	3466 ms	100K
Agentic Coding	S	Runs well	55.9 tok/s	5041 ms	100K
Reasoning	S	Runs well	55.9 tok/s	4096 ms	100K
RAG	S	Runs well	55.9 tok/s	6302 ms	100K

Inference speed

Gemma 4 26B A4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 26B A4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~195 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	195.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	124.4	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.2	Tight
RTX 3090 24GB	24 GB	Q4_K_M	106.4	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	90.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	75.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	71.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	55.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	55.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	39.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	38.7	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	35.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	34.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	13.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	8.5	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.8	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	A78
Q3_K_S	3	12.3 GB	Low	A79
NVFP4	4	14.1 GB	Medium	A80
Q4_K_M	4	15.4 GB	Medium	A80
Q5_K_M	5	18.1 GB	High	A81
Q6_K	6	20.7 GB	High	A82
Q8_0Best for your GPU	8	27.0 GB	Very High	A83
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 M4 Max 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	52 tok/s
Qwen 3.5 27B	27B	S	36.1 tok/s
Qwen 3.6 27B	27B	S	27.4 tok/s
Qwen 3.6 35B A3B	35B	S	43.7 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.8 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Gemma 4 26B A4B?

Yes, MacBook Pro M4 Max 64GB can run Gemma 4 26B A4B with a S grade (Runs well). Expected decode speed: 55.9 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 26.8 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 M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Gemma 4 26B A4B achieves approximately 55.9 tokens per second decode speed with a time-to-first-token of 3466ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on MacBook Pro M4 Max 64GB receives a S grade with 55.9 tok/s and 100K context.

What context window can Gemma 4 26B A4B use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Gemma 4 26B A4B can safely use up to 100K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Gemma 4 26B A4B?

Not always. MacBook Pro M4 Max 64GB 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.

See all results for MacBook Pro M4 Max 64GB See all hardware for Gemma 4 26B A4B

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