Can gemma 3 12b it run on MacBook Pro M4 Max 128GB?

Q: Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for gemma 3 12b it?

Not always. MacBook Pro M4 Max 128GB 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.

YES — Runs Great

C45Usable

Estimated from fit model

gemma 3 12b it needs ~23.5 GB VRAM. MacBook Pro M4 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~47 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) — 23.5 GB, 41.4 tok/s, Runs well

23.5 GB required92.2 GB available

25% VRAM used

Fit status

Runs well

Decode

41.4 tok/s

TTFT

4682 ms

Safe context

798K

Memory

23.5 GB / 92.2 GB

Memory breakdown

Weights7.3 GB

KV Cache1.4 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsgemma 3 12b it on MacBook Pro M4 Max 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 41.4 tok/s decode · 4.7s TTFT (warm) · 103 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	C	Runs well	41.4 tok/s	2554 ms	798K
Coding	C	Runs well	47.0 tok/s	4120 ms	798K
Agentic Coding	C	Runs well	41.4 tok/s	6810 ms	798K
Reasoning	C	Runs well	41.4 tok/s	5533 ms	798K
RAG	C	Runs well	41.4 tok/s	8512 ms	798K

Quantization options

How gemma 3 12b it (12B params) fits at each quantization level on MacBook Pro M4 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	D39
Q3_K_S	3	5.9 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run gemma 3 12b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-12b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 3 12b it well

Mac Studio M3 Ultra 256GBBudget pick

256 GB Unified (+128)819 GB/s (+273)

Raises estimated decode speed by about 84%.76.1 tok/s decode

Raises estimated decode speed by about 84%.

Adds memory headroom for longer context windows and future model growth.

~$6,999 MSRP

RTX PRO 6000 Blackwell Workstation Edition 96GBBest value

1792 GB/s (+1246)

Raises estimated decode speed by about 306%.168 tok/s decode

Raises estimated decode speed by about 306%.

~$9,999 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 128GB run gemma 3 12b it?

Yes, MacBook Pro M4 Max 128GB can run gemma 3 12b it with a C grade (Runs well). Expected decode speed: 47.0 tok/s.

How much VRAM does gemma 3 12b it need?

gemma 3 12b it (12B parameters) requires approximately 23.5 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 12b it?

The recommended quantization for gemma 3 12b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 12b it run at on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, gemma 3 12b it achieves approximately 47.0 tokens per second decode speed with a time-to-first-token of 4120ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 128GB run gemma 3 12b it for coding?

For coding workloads, gemma 3 12b it on MacBook Pro M4 Max 128GB receives a C grade with 47.0 tok/s and 798K context.

What context window can gemma 3 12b it use on MacBook Pro M4 Max 128GB?

On MacBook Pro M4 Max 128GB, gemma 3 12b it can safely use up to 798K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 128GB as fast as VRAM for gemma 3 12b it?

See all results for MacBook Pro M4 Max 128GB See all hardware for gemma 3 12b it

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