Can Gemma 4 12B run on MacBook Pro M3 Pro 18GB?

YES — With NVFP4

B62Good

Estimated from fit model

Gemma 4 12B needs ~15.4 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With NVFP4 quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very 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.

Gemma 4 12B at Q4_K_M needs 16.0 GB — too much for MacBook Pro M3 Pro 18GB (13.0 GB). Runs at NVFP4 (15.4 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 16.0 GB, exceeds 13.0 GB available

16.0 GB required13.0 GB available

123% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.7 tok/s

TTFT

22163 ms

Safe context

Memory

16.0 GB / 13.0 GB

Offload

20%

Memory breakdown

Weights7.3 GB

KV Cache5.9 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGemma 4 12B on MacBook Pro M3 Pro 18GB
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: 8.7 tok/s decode · 22.2s TTFT (warm) · 22 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 20% 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 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.1 GB host RAM)	11.6 tok/s	9104 ms	8K
Coding	F	Too heavy	8.7 tok/s	22163 ms	8K
Agentic Coding	F	Too heavy	6.1 tok/s	46194 ms	8K
Reasoning	F	Too heavy	8.7 tok/s	26193 ms	8K
RAG	F	Too heavy	6.1 tok/s	57742 ms	8K

Inference speed

Gemma 4 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Gemma 4 12B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~168 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	168.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	109.9	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	99.1	Fits
RTX 3090 24GB	24 GB	Q4_K_M	94.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	87.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.5	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.4	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	47.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	43.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	34.4	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	32.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	31.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	26.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	23.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	14.8	Too big
RTX 4060 8GB	8 GB	Q4_K_M	5.5	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 12B (12B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A82
Q3_K_S	3	5.9 GB	Low	A84
NVFP4	4	6.7 GB	Medium	A83
Q4_K_M	4	7.3 GB	Medium	A83
Q5_K_M	5	8.6 GB	High	A83
Q6_KBest for your GPU	6	9.8 GB	High	A83
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 4 12B on your machine.

Run

lms load gemma-4-12B-it && lms server start

Upgrade-Optionen

Hardware, die Gemma 4 12B gut ausführt

MacBook Pro M4 32GBGünstige Wahl

32 GB Unified (+14)

Makes the model fit on the accelerator instead of staying completely out of reach.8.3 tok/s Dekodierung

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.

ca. $799 MSRP

Mac mini M4 32GBBestes Preis-Leistungs-Verhältnis

32 GB Unified (+14)

Makes the model fit on the accelerator instead of staying completely out of reach.8.3 tok/s Dekodierung

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.

ca. $1,099 MSRP

MacBook Air M4 24GBApple-Upgrade

24 GB Unified (+6)

Makes the model fit on the accelerator instead of staying completely out of reach.8.3 tok/s Dekodierung

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.

ca. $1,099 MSRP

RTX 5090 Laptop 24GBGrößter Sprung

24 GB VRAM (+6)896 GB/s (+746)

Makes the model fit on the accelerator instead of staying completely out of reach.74.7 tok/s Dekodierung

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.

Frequently asked questions

Can MacBook Pro M3 Pro 18GB run Gemma 4 12B?

Yes, MacBook Pro M3 Pro 18GB can run Gemma 4 12B at NVFP4 quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 16.0 GB which exceeds available memory, but at NVFP4 it needs only 15.4 GB. Expected decode speed: 10.5 tok/s.

How much VRAM does Gemma 4 12B need?

Gemma 4 12B (12B parameters) requires approximately 16.0 GB at Q4_K_M quantization. On MacBook Pro M3 Pro 18GB, it fits at NVFP4 using 15.4 GB.

What is the best quantization for Gemma 4 12B?

The recommended quantization is Q4_K_M, but on MacBook Pro M3 Pro 18GB the best fitting quantization is NVFP4, which uses 15.4 GB.

What speed will Gemma 4 12B run at on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Gemma 4 12B achieves approximately 10.5 tokens per second decode speed with a time-to-first-token of 18479ms using NVFP4 quantization.

Can MacBook Pro M3 Pro 18GB run Gemma 4 12B for coding?

For coding workloads, Gemma 4 12B on MacBook Pro M3 Pro 18GB receives a F grade with 8.7 tok/s and 8K context.

What context window can Gemma 4 12B use on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Gemma 4 12B can safely use up to 9K tokens of context at NVFP4 quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Gemma 4 12B feels slow on MacBook Pro M3 Pro 18GB?

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 Pro 18GB as fast as VRAM for Gemma 4 12B?

Not always. MacBook Pro M3 Pro 18GB 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 Pro 18GB See all hardware for Gemma 4 12B

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