Can EXAONE 4.0 32B run on MacBook Pro M3 Max 48GB?

YES — Tight Fit

A83Great

Estimated from fit model

EXAONE 4.0 32B needs ~29.5 GB VRAM. MacBook Pro M3 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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, 13.3 tok/s, Tight fit

29.5 GB required34.6 GB available

85% VRAM used

Fit status

Tight fit

Decode

13.3 tok/s

TTFT

14580 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 M3 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: 13.3 tok/s decode · 14.6s TTFT (warm) · 33 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	13.3 tok/s	7953 ms	37K
Coding	A	Tight fit	13.3 tok/s	14580 ms	37K
Agentic Coding	A	Runs with offload	13.3 tok/s	21207 ms	37K
Reasoning	A	Tight fit	13.3 tok/s	17231 ms	37K
RAG	A	Runs with offload	13.3 tok/s	26509 ms	37K

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 MacBook Pro M3 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	17.9 GB	Medium	A84
Q4_K_M	4	19.5 GB	Medium	A84
Q5_K_M	5	23.0 GB	High	A84
Q6_KBest for your GPU	6	26.2 GB	High	A83
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 M3 Max 48GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.6 35B A3B	35B	S	33.5 tok/s
Qwen 3.5 35B A3B	35B	S	36.5 tok/s

Frequently asked questions

Can MacBook Pro M3 Max 48GB run EXAONE 4.0 32B?

Yes, MacBook Pro M3 Max 48GB can run EXAONE 4.0 32B with a A grade (Tight fit). Expected decode speed: 13.3 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 29.5 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 M3 Max 48GB?

On MacBook Pro M3 Max 48GB, EXAONE 4.0 32B achieves approximately 13.3 tokens per second decode speed with a time-to-first-token of 14580ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 48GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on MacBook Pro M3 Max 48GB receives a A grade with 13.3 tok/s and 37K context.

What context window can EXAONE 4.0 32B use on MacBook Pro M3 Max 48GB?

On MacBook Pro M3 Max 48GB, EXAONE 4.0 32B can safely use up to 37K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 48GB as fast as VRAM for EXAONE 4.0 32B?

Not always. MacBook Pro M3 Max 48GB 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 M3 Max 48GB See all hardware for EXAONE 4.0 32B

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