Can EXAONE 4.0 32B run on MacBook Pro M4 Max 36GB?

BARELY — Tight on Memory

A74Great

Estimated from fit model

EXAONE 4.0 32B needs ~28.2 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 28.2 GB, 22.9 tok/s, Very compromised (needs ~1.6 GB host RAM)

28.2 GB required25.9 GB available

109% VRAM needed

2.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.6 GB host RAM)

Decode

22.9 tok/s

TTFT

8441 ms

Safe context

Memory

28.2 GB / 25.9 GB

Offload

10%

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on MacBook Pro M4 Max 36GB
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: 22.9 tok/s decode · 8.4s TTFT (warm) · 57 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload (needs ~0.3 GB host RAM)	25.7 tok/s	4113 ms	7K
Coding	A	Very compromised (needs ~1.6 GB host RAM)	22.9 tok/s	8441 ms	7K
Agentic Coding	F	Too heavy	19.4 tok/s	14524 ms	7K
Reasoning	A	Very compromised (needs ~1.6 GB host RAM)	22.9 tok/s	9975 ms	7K
RAG	F	Too heavy	19.4 tok/s	18155 ms	7K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	S85
Q3_K_S	3	15.7 GB	Low	A85
NVFP4	4	17.9 GB	Medium	A84
Q4_K_MBest for your GPU	4	19.5 GB	Medium	A84
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
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 MacBook Pro M4 Max 36GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	A	28.5 tok/s
Qwen 3.5 35B A3B	35B	A	35.1 tok/s

Frequently asked questions

Can MacBook Pro M4 Max 36GB run EXAONE 4.0 32B?

Yes, MacBook Pro M4 Max 36GB can run EXAONE 4.0 32B with a A grade (Very compromised (needs ~1.6 GB host RAM)). Expected decode speed: 22.9 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 28.2 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 MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, EXAONE 4.0 32B achieves approximately 22.9 tokens per second decode speed with a time-to-first-token of 8441ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 36GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on MacBook Pro M4 Max 36GB receives a A grade with 22.9 tok/s and 7K context.

What context window can EXAONE 4.0 32B use on MacBook Pro M4 Max 36GB?

On MacBook Pro M4 Max 36GB, EXAONE 4.0 32B can safely use up to 7K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if EXAONE 4.0 32B feels slow on MacBook Pro M4 Max 36GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Is unified memory on MacBook Pro M4 Max 36GB as fast as VRAM for EXAONE 4.0 32B?

Not always. MacBook Pro M4 Max 36GB 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 MacBook Pro M4 Max 36GB See all hardware for EXAONE 4.0 32B

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