Can EXAONE 4.0 32B run on Mac Studio M3 Ultra 256GB?

YES — Runs Great

A79Great

Estimated from fit model

EXAONE 4.0 32B needs ~52.0 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~31 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) — 52.0 GB, 30.8 tok/s, Runs well

52.0 GB required184.3 GB available

28% VRAM used

Fit status

Runs well

Decode

30.8 tok/s

TTFT

6283 ms

Safe context

131K

Memory

52.0 GB / 184.3 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom27.6 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on Mac Studio M3 Ultra 256GB
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: 30.8 tok/s decode · 6.3s TTFT (warm) · 77 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	30.8 tok/s	3427 ms	131K
Coding	A	Runs well	30.8 tok/s	6283 ms	131K
Agentic Coding	A	Runs well	30.8 tok/s	9139 ms	131K
Reasoning	A	Runs well	30.8 tok/s	7425 ms	131K
RAG	A	Runs well	30.8 tok/s	11424 ms	131K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A72
Q3_K_S	3	15.7 GB	Low	A72
NVFP4	4	17.9 GB	Medium	A72
Q4_K_M	4	19.5 GB	Medium	A73
Q5_K_M	5	23.0 GB	High	A73
Q6_K	6	26.2 GB	High	A73
Q8_0	8	34.2 GB	Very High	A74
F16Best for your GPU	16	65.6 GB	Maximum	A78

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 M3 Ultra 256GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	8.1 tok/s
Qwen 3.5 122B A10B	122B	S	34.7 tok/s
DeepSeek V4 Flash	284B	S	17.8 tok/s
Qwen 3.6 35B A3B	35B	S	70.8 tok/s
Qwen 3.5 35B A3B	35B	S	77 tok/s

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run EXAONE 4.0 32B?

Yes, Mac Studio M3 Ultra 256GB can run EXAONE 4.0 32B with a A grade (Runs well). Expected decode speed: 30.8 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 52.0 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 M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, EXAONE 4.0 32B achieves approximately 30.8 tokens per second decode speed with a time-to-first-token of 6283ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Mac Studio M3 Ultra 256GB receives a A grade with 30.8 tok/s and 131K context.

What context window can EXAONE 4.0 32B use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, EXAONE 4.0 32B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for EXAONE 4.0 32B?

Not always. Mac Studio M3 Ultra 256GB 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 M3 Ultra 256GB See all hardware for EXAONE 4.0 32B

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