Can EXAONE 4.0 32B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

S87Excellent

Estimated from fit model

EXAONE 4.0 32B needs ~31.2 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~24 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) — 31.2 GB, 25.7 tok/s, Runs well

31.2 GB required46.1 GB available

68% VRAM used

Fit status

Runs well

Decode

25.7 tok/s

TTFT

7541 ms

Safe context

77K

Memory

31.2 GB / 46.1 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B 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: 25.7 tok/s decode · 7.5s TTFT (warm) · 64 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	S	Runs well	25.7 tok/s	4113 ms	77K
Coding	S	Runs well	23.8 tok/s	8145 ms	77K
Agentic Coding	S	Runs well	25.7 tok/s	10969 ms	77K
Reasoning	S	Runs well	25.7 tok/s	8912 ms	77K
RAG	S	Runs well	25.7 tok/s	13711 ms	77K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A79
Q3_K_S	3	15.7 GB	Low	A80
NVFP4	4	17.9 GB	Medium	A81
Q4_K_M	4	19.5 GB	Medium	A82
Q5_K_M	5	23.0 GB	High	A83
Q6_K	6	26.2 GB	High	A83
Q8_0Best for your GPU	8	34.2 GB	Very High	A83
F16	16	65.6 GB	Maximum	F0

Get started

Copy-paste commands to run EXAONE 4.0 32B on your machine.

Run

ollama run exaone-4:32b

Your hardware

More models your Mac Studio M2 Ultra 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	59 tok/s
Qwen 3.5 35B A3B	35B	S	64.1 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run EXAONE 4.0 32B?

Yes, Mac Studio M2 Ultra 64GB can run EXAONE 4.0 32B with a S grade (Runs well). Expected decode speed: 23.8 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 31.2 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 4.0 32B?

The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 4.0 32B run at on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, EXAONE 4.0 32B achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8145ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Mac Studio M2 Ultra 64GB receives a S grade with 23.8 tok/s and 77K context.

What context window can EXAONE 4.0 32B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, EXAONE 4.0 32B can safely use up to 77K 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 EXAONE 4.0 32B?

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 EXAONE 4.0 32B

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