Can Gemma 4 31B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

A85Great

Estimated from fit model

Gemma 4 31B needs ~48.1 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~20 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) — 48.1 GB, 19.7 tok/s, Runs well

48.1 GB required92.2 GB available

52% VRAM used

Fit status

Runs well

Decode

19.7 tok/s

TTFT

9819 ms

Safe context

64K

Memory

48.1 GB / 92.2 GB

Memory breakdown

Weights18.7 GB

KV Cache14.6 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsGemma 4 31B on Mac Studio M2 Ultra 128GB
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: 19.7 tok/s decode · 9.8s TTFT (warm) · 49 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	19.7 tok/s	5356 ms	64K
Coding	A	Runs well	19.7 tok/s	9819 ms	64K
Agentic Coding	S	Runs well	19.7 tok/s	14283 ms	64K
Reasoning	A	Runs well	19.7 tok/s	11605 ms	64K
RAG	S	Runs well	19.7 tok/s	17853 ms	64K

Quantization options

How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.0 GB	Low	A77
Q3_K_S	3	15.0 GB	Low	A77
NVFP4	4	17.2 GB	Medium	A77
Q4_K_M	4	18.7 GB	Medium	A78
Q5_K_M	5	22.1 GB	High	A78
Q6_K	6	25.2 GB	High	A79
Q8_0	8	32.8 GB	Very High	A80
F16Best for your GPU	16	62.9 GB	Maximum	A85

Get started

Copy-paste commands to run Gemma 4 31B on your machine.

Run

ollama run gemma4:31b

Your hardware

More models your Mac Studio M2 Ultra 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	6.3 tok/s
Qwen 3.5 122B A10B	122B	S	28.9 tok/s
Qwen 3.6 35B A3B	35B	S	59 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 128GB run Gemma 4 31B?

Yes, Mac Studio M2 Ultra 128GB can run Gemma 4 31B with a A grade (Runs well). Expected decode speed: 19.7 tok/s.

How much VRAM does Gemma 4 31B need?

Gemma 4 31B (30.700000762939453B parameters) requires approximately 48.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Gemma 4 31B?

The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.

What speed will Gemma 4 31B run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Gemma 4 31B achieves approximately 19.7 tokens per second decode speed with a time-to-first-token of 9819ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Gemma 4 31B for coding?

For coding workloads, Gemma 4 31B on Mac Studio M2 Ultra 128GB receives a A grade with 19.7 tok/s and 64K context.

What context window can Gemma 4 31B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Gemma 4 31B can safely use up to 64K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Gemma 4 31B?

Not always. Mac Studio M2 Ultra 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.

See all results for Mac Studio M2 Ultra 128GB See all hardware for Gemma 4 31B

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