Will It Run AI

Can Gemma 4 26B A4B run on MacBook Pro M3 24GB?

YES — With Q3_K_S

A72Great
Estimated from fit model

Gemma 4 26B A4B needs ~19.5 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q3_K_S quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
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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.

Gemma 4 26B A4B at Q4_K_M needs 22.5 GB — too much for MacBook Pro M3 24GB (17.3 GB). Runs at Q3_K_S (19.5 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.5 GB, exceeds 17.3 GB available
22.5 GB required17.3 GB available
130% VRAM needed

5.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.6 tok/s

TTFT

25472 ms

Safe context

4K

Memory

22.5 GB / 17.3 GB

Offload

20%

Memory breakdown

Weights15.4 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom2.6 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 M3 24GB
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: 7.6 tok/s decode · 25.5s TTFT (warm) · 19 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 1.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBVery compromised (needs ~2.5 GB host RAM)8.4 tok/s12527 ms4K
CodingFToo heavy7.6 tok/s25472 ms4K
Agentic CodingFToo heavy6.4 tok/s44123 ms4K
ReasoningFToo heavy7.6 tok/s30103 ms4K
RAGFToo heavy6.4 tok/s55154 ms4K

Quantization options

How Gemma 4 26B A4B (25.200000762939453B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
9.8 GB
LowS86
Q3_K_SBest for your GPU
3
12.3 GB
LowS85
NVFP4
4
14.1 GB
MediumF0
Q4_K_M
4
15.4 GB
MediumF0
Q5_K_M
5
18.1 GB
HighF0
Q6_K
6
20.7 GB
HighF0
Q8_0
8
27.0 GB
Very HighF0
F16
16
51.7 GB
MaximumF0

Get started

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

Run

ollama run gemma4:26b

Opciones de mejora

Hardware que ejecuta bien Gemma 4 26B A4B

MacBook Pro M4 32GBOpción económica
32 GB Unified (+8)120 GB/s (+20)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.13.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$799 MSRP

Mac mini M4 64GBMejor relación calidad-precio
64 GB Unified (+40)120 GB/s (+20)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.14 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,099 MSRP

Mac mini M4 32GBMejora Apple
32 GB Unified (+8)120 GB/s (+20)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.13.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,099 MSRP

NVIDIARTX 5090 32GBMayor salto
32 GB VRAM (+8)1792 GB/s (+1692)
S
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.175.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,999 MSRP

Frequently asked questions

Can MacBook Pro M3 24GB run Gemma 4 26B A4B?

Yes, MacBook Pro M3 24GB can run Gemma 4 26B A4B at Q3_K_S quantization (Very compromised (needs ~1.4 GB host RAM)). The recommended Q4_K_M requires 22.5 GB which exceeds available memory, but at Q3_K_S it needs only 19.5 GB. Expected decode speed: 10.5 tok/s.

How much VRAM does Gemma 4 26B A4B need?

Gemma 4 26B A4B (25.200000762939453B parameters) requires approximately 22.5 GB at Q4_K_M quantization. On MacBook Pro M3 24GB, it fits at Q3_K_S using 19.5 GB.

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

The recommended quantization is Q4_K_M, but on MacBook Pro M3 24GB the best fitting quantization is Q3_K_S, which uses 19.5 GB.

What speed will Gemma 4 26B A4B run at on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, Gemma 4 26B A4B achieves approximately 10.5 tokens per second decode speed with a time-to-first-token of 18377ms using Q3_K_S quantization.

Can MacBook Pro M3 24GB run Gemma 4 26B A4B for coding?

For coding workloads, Gemma 4 26B A4B on MacBook Pro M3 24GB receives a F grade with 7.6 tok/s and 4K context.

What context window can Gemma 4 26B A4B use on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, Gemma 4 26B A4B can safely use up to 6K tokens of context at Q3_K_S quantization. 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 M3 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M3 24GB as fast as VRAM for Gemma 4 26B A4B?

Not always. MacBook Pro M3 24GB 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 M3 24GBSee all hardware for Gemma 4 26B A4B
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