Can Gemma 3 27B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

A85Great

Estimated from fit model

Gemma 3 27B needs ~35.5 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 35.5 GB, 22.4 tok/s, Runs well

35.5 GB required46.1 GB available

77% VRAM used

Fit status

Runs well

Decode

22.4 tok/s

TTFT

8636 ms

Safe context

31K

Memory

35.5 GB / 46.1 GB

Memory breakdown

Weights16.5 GB

KV Cache11.2 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsGemma 3 27B on Mac Studio M2 Ultra 64GB
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: 22.4 tok/s decode · 8.6s TTFT (warm) · 56 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	A	Runs well	22.4 tok/s	4710 ms	31K
Coding	A	Runs well	22.4 tok/s	8636 ms	31K
Agentic Coding	A	Runs with offload (needs ~0.2 GB host RAM)	21.7 tok/s	12993 ms	31K
Reasoning	A	Runs well	22.4 tok/s	10206 ms	31K
RAG	A	Runs with offload (needs ~0.2 GB host RAM)	21.7 tok/s	16241 ms	31K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A76
Q3_K_S	3	13.2 GB	Low	A77
NVFP4	4	15.1 GB	Medium	A77
Q4_K_M	4	16.5 GB	Medium	A78
Q5_K_M	5	19.4 GB	High	A79
Q6_K	6	22.1 GB	High	A80
Q8_0Best for your GPU	8	28.9 GB	Very High	A81
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Gemma 3 27B on your machine.

Run

ollama run gemma3

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.2 tok/s
Qwen 3.6 35B A3B	35B	S	59 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.6 tok/s
Qwen 3.5 35B A3B	35B	S	64.1 tok/s
Qwen 3 32B	32B	S	25.9 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Gemma 3 27B?

Yes, Mac Studio M2 Ultra 64GB can run Gemma 3 27B with a A grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 35.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Gemma 3 27B achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8636ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on Mac Studio M2 Ultra 64GB receives a A grade with 22.4 tok/s and 31K context.

What context window can Gemma 3 27B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Gemma 3 27B can safely use up to 31K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Gemma 3 27B?

Not always. Mac Studio M2 Ultra 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.

See all results for Mac Studio M2 Ultra 64GB See all hardware for Gemma 3 27B

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<iframe src="https://willitrunai.com/embed/gemma-3-27b-on-m2-ultra-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

22.4 tok/s

4710 ms

31K

Coding

Runs well

22.4 tok/s

8636 ms

31K

Agentic Coding

Runs with offload (needs ~0.2 GB host RAM)

21.7 tok/s

12993 ms

31K

Reasoning

Runs well

22.4 tok/s

10206 ms

31K

RAG

Runs with offload (needs ~0.2 GB host RAM)

21.7 tok/s

16241 ms

31K

Quantization options

How Gemma 3 27B (27B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	A76
Q3_K_S	3	13.2 GB	Low	A77
NVFP4	4	15.1 GB	Medium	A77
Q4_K_M	4	16.5 GB	Medium	A78
Q5_K_M	5	19.4 GB	High	A79
Q6_K	6	22.1 GB	High	A80
Q8_0Best for your GPU	8	28.9 GB	Very High	A81
F16	16	55.4 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

70.2 tok/s

Qwen 3.6 35B A3B

35B

59 tok/s

Qwen3-VL 30B A3B Instruct

30B

72.6 tok/s

Qwen 3.5 35B A3B

35B

64.1 tok/s

Qwen 3 32B

32B

25.9 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run Gemma 3 27B?

Yes, Mac Studio M2 Ultra 64GB can run Gemma 3 27B with a A grade (Runs well). Expected decode speed: 22.4 tok/s.

How much VRAM does Gemma 3 27B need?

Gemma 3 27B (27B parameters) requires approximately 35.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 3 27B?

The recommended quantization for Gemma 3 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 3 27B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Gemma 3 27B achieves approximately 22.4 tokens per second decode speed with a time-to-first-token of 8636ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run Gemma 3 27B for coding?

For coding workloads, Gemma 3 27B on Mac Studio M2 Ultra 64GB receives a A grade with 22.4 tok/s and 31K context.

What context window can Gemma 3 27B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, Gemma 3 27B can safely use up to 31K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for Gemma 3 27B?