Can Gemma 4 26B A4B run on MacBook Pro M4 16GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Gemma 4 26B A4B needs ~21.7 GB but MacBook Pro M4 16GB only has 11.5 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 21.7 GB, exceeds 11.5 GB available

21.7 GB required11.5 GB available

189% VRAM needed

10.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.4 tok/s

TTFT

30322 ms

Safe context

Memory

21.7 GB / 11.5 GB

Offload

50%

Memory breakdown

Weights15.4 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 26B A4B on MacBook Pro M4 16GB
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: 6.4 tok/s decode · 30.3s TTFT (warm) · 16 tok/s prefill

What limits this setup

Usable shared or unified memory is the main blocker for this model.

Not enough usable memory

The model needs 21.7 GB, but this setup only exposes 11.5 GB of usable shared or unified memory.

Best improvement path

Move to a larger memory pool

A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	7.0 tok/s	15014 ms	4K
Coding	F	Too heavy	6.4 tok/s	30322 ms	4K
Agentic Coding	F	Too heavy	6.3 tok/s	44602 ms	4K
Reasoning	F	Too heavy	6.4 tok/s	35835 ms	4K
RAG	F	Too heavy	6.3 tok/s	55752 ms	4K

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 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	F0
Q3_K_S	3	12.3 GB	Low	F0
NVFP4	4	14.1 GB	Medium	F0
Q4_K_M	4	15.4 GB	Medium	F0
Q5_K_M	5	18.1 GB	High	F0
Q6_K	6	20.7 GB	High	F0
Q8_0	8	27.0 GB	Very High	F0
F16	16	51.7 GB	Maximum	F0

Upgrade options

Hardware that runs Gemma 4 26B A4B well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.13.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$799 MSRP

Mac mini M4 64GBBest value

64 GB Unified (+48)

Makes the model fit on the accelerator instead of staying completely out of reach.14 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 32GBApple upgrade

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.13.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

RTX 5090 32GBBiggest leap

32 GB VRAM (+16)1792 GB/s (+1672)

Makes the model fit on the accelerator instead of staying completely out of reach.175.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M4 16GB run Gemma 4 26B A4B?

No, Gemma 4 26B A4B requires more memory than MacBook Pro M4 16GB provides.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 21.7 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 16GB?

On MacBook Pro M4 16GB, Gemma 4 26B A4B achieves approximately 6.4 tokens per second decode speed with a time-to-first-token of 30322ms using Q4_K_M quantization.

Can MacBook Pro M4 16GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on MacBook Pro M4 16GB receives a F grade with 6.4 tok/s and 4K context.

What context window can Gemma 4 26B A4B use on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, Gemma 4 26B A4B can safely use up to 4K 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 M4 16GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Is unified memory on MacBook Pro M4 16GB as fast as VRAM for Gemma 4 26B A4B?

Not always. MacBook Pro M4 16GB 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 16GB See all hardware for Gemma 4 26B A4B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

7.0 tok/s

15014 ms

Coding

Too heavy

6.4 tok/s

30322 ms

Agentic Coding

Too heavy

6.3 tok/s

44602 ms

Reasoning

Too heavy

6.4 tok/s

35835 ms

RAG

Too heavy

6.3 tok/s

55752 ms

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

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

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.8 GB	Low	F0
Q3_K_S	3	12.3 GB	Low	F0
NVFP4	4	14.1 GB	Medium	F0
Q4_K_M	4	15.4 GB	Medium	F0
Q5_K_M	5	18.1 GB	High	F0
Q6_K	6	20.7 GB	High	F0
Q8_0	8	27.0 GB	Very High	F0
F16	16	51.7 GB	Maximum	F0

Frequently asked questions

Can MacBook Pro M4 16GB run Gemma 4 26B A4B?

No, Gemma 4 26B A4B requires more memory than MacBook Pro M4 16GB provides.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 21.7 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 16GB?

On MacBook Pro M4 16GB, Gemma 4 26B A4B achieves approximately 6.4 tokens per second decode speed with a time-to-first-token of 30322ms using Q4_K_M quantization.

Can MacBook Pro M4 16GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on MacBook Pro M4 16GB receives a F grade with 6.4 tok/s and 4K context.

What context window can Gemma 4 26B A4B use on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, Gemma 4 26B A4B can safely use up to 4K 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 M4 16GB?

Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.

Is unified memory on MacBook Pro M4 16GB as fast as VRAM for Gemma 4 26B A4B?