Can gemma 3 27b it run on Mac mini M4 64GB?

Q: Is unified memory on Mac mini M4 64GB as fast as VRAM for gemma 3 27b it?

Not always. Mac mini M4 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.

YES — Runs Great

C47Usable

Estimated — low-sample bucket· few comparable runs

gemma 3 27b it needs ~27.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 27.4 GB, 8.7 tok/s, Runs well

27.4 GB required46.1 GB available

59% VRAM used

Fit status

Runs well

Decode

8.7 tok/s

TTFT

22367 ms

Safe context

110K

Memory

27.4 GB / 46.1 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsgemma 3 27b it on Mac mini M4 64GB
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: 8.7 tok/s decode · 22.4s TTFT (warm) · 22 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	8.7 tok/s	12200 ms	110K
Coding	C	Runs well	8.7 tok/s	22367 ms	110K
Agentic Coding	C	Runs well	8.7 tok/s	32533 ms	110K
Reasoning	C	Runs well	8.7 tok/s	26433 ms	110K
RAG	C	Runs well	8.7 tok/s	40667 ms	110K

Quantization options

How gemma 3 27b it (27B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C43
Q3_K_S	3	13.2 GB	Low	C44
NVFP4	4

Get started

Copy-paste commands to run gemma 3 27b it on your machine.

Run

lms load hf-maziyarpanahi--gemma-3-27b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 3 27b it well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 284%.33.4 tok/s decode

Raises estimated decode speed by about 284%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 289%.33.8 tok/s decode

Raises estimated decode speed by about 289%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run gemma 3 27b it?

Yes, Mac mini M4 64GB can run gemma 3 27b it with a C grade (Runs well). Expected decode speed: 8.7 tok/s.

How much VRAM does gemma 3 27b it need?

gemma 3 27b it (27B parameters) requires approximately 27.4 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 27b it?

The recommended quantization for gemma 3 27b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 27b it run at on Mac mini M4 64GB?

On Mac mini M4 64GB, gemma 3 27b it achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22367ms using Q4_K_M quantization.

Can Mac mini M4 64GB run gemma 3 27b it for coding?

For coding workloads, gemma 3 27b it on Mac mini M4 64GB receives a C grade with 8.7 tok/s and 110K context.

What context window can gemma 3 27b it use on Mac mini M4 64GB?

On Mac mini M4 64GB, gemma 3 27b it can safely use up to 110K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for gemma 3 27b it?

See all results for Mac mini M4 64GB See all hardware for gemma 3 27b it

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