Can EXAONE 4.0 32B run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

A82Great

Estimated from fit model

EXAONE 4.0 32B needs ~38.2 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~26 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) — 38.2 GB, 25.7 tok/s, Runs well

38.2 GB required92.2 GB available

41% VRAM used

Fit status

Runs well

Decode

25.7 tok/s

TTFT

7541 ms

Safe context

131K

Memory

38.2 GB / 92.2 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B 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: 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	A	Runs well	25.7 tok/s	4113 ms	131K
Coding	A	Runs well	25.7 tok/s	7541 ms	131K
Agentic Coding	A	Runs well	25.7 tok/s	10969 ms	131K
Reasoning	A	Runs well	25.7 tok/s	8912 ms	131K
RAG	A	Runs well	25.7 tok/s	13711 ms	131K

Inference speed

EXAONE 4.0 32B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for EXAONE 4.0 32B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~66 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	66.4	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	33.2	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	30.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	25.7	Fits
RTX 4090 24GB	24 GB	Q4_K_M	24.8	Heavy offload
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	24.3	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	22.9	Heavy offload
RTX 3090 24GB	24 GB	Q4_K_M	21.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	20.9	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	13.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	9.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.1	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A75
Q3_K_S	3	15.7 GB	Low	A75
NVFP4	4	17.9 GB	Medium	A76
Q4_K_M	4	19.5 GB	Medium	A76
Q5_K_M	5	23.0 GB	High	A76
Q6_K	6	26.2 GB	High	A77
Q8_0	8	34.2 GB	Very High	A79
F16Best for your GPU	16	65.6 GB	Maximum	A83

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 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
Mistral Small 4 119B	119B	S	30.8 tok/s

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run EXAONE 4.0 32B?

Yes, Mac Studio M2 Ultra 128GB can run EXAONE 4.0 32B with a A grade (Runs well). Expected decode speed: 25.7 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 38.2 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 128GB?

On Mac Studio M2 Ultra 128GB, EXAONE 4.0 32B achieves approximately 25.7 tokens per second decode speed with a time-to-first-token of 7541ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Mac Studio M2 Ultra 128GB receives a A grade with 25.7 tok/s and 131K context.

What context window can EXAONE 4.0 32B use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, 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 M2 Ultra 128GB as fast as VRAM for EXAONE 4.0 32B?

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 EXAONE 4.0 32B

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