Can EXAONE 4.0 32B run on MacBook Pro M4 Max 48GB?

YES — Tight Fit

A84Great

○Estimated from fit model

EXAONE 4.0 32B needs ~29.5 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 29.5 GB, 33.2 tok/s, Tight fit

29.5 GB required34.6 GB available

85% VRAM used

Fit status

Tight fit

Decode

33.2 tok/s

TTFT

5826 ms

Safe context

37K

Memory

29.5 GB / 34.6 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on MacBook Pro M4 Max 48GB
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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 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	33.2 tok/s	3178 ms	37K
Coding	A	Tight fit	17.6 tok/s	10986 ms	37K
Agentic Coding	S	Runs with offload	33.2 tok/s	8474 ms	37K
Reasoning	S	Tight fit	33.2 tok/s	6885 ms	37K
RAG	S	Runs with offload	33.2 tok/s	10593 ms	37K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A82
Q3_K_S	3	15.7 GB	Low	A83
NVFP4	4

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 48GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	43.7 tok/s
👁 Alibaba

Frequently asked questions

See all results for MacBook Pro M4 Max 48GB See all hardware for EXAONE 4.0 32B

URL: https://willitrunai.com/can-run/exaone-4-32b-on-m4-max-48gb

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