Can EXAONE 4.0 32B run on Mac mini M4 64GB?

Q: Is unified memory on Mac mini M4 64GB as fast as VRAM for EXAONE 4.0 32B?

Not always. Mac mini M4 64GB 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.

YES — Runs Great

C48Usable

Estimated — low-sample bucket· few comparable runs

EXAONE 4.0 32B needs ~31.1 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 31.1 GB, 8.0 tok/s, Runs well

31.1 GB required46.1 GB available

67% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24300 ms

Safe context

80K

Memory

31.1 GB / 46.1 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on Mac mini M4 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 8.0 tok/s decode · 24.3s TTFT (warm) · 20 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	C	Runs well	8.0 tok/s	13254 ms	80K
Coding	C	Runs well	8.0 tok/s	24300 ms	80K
Agentic Coding	C	Runs well	8.0 tok/s	35345 ms	80K
Reasoning	C	Runs well	8.0 tok/s	28718 ms	80K
RAG	C	Runs well	8.0 tok/s	44181 ms	80K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C44
Q3_K_S	3	15.7 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run EXAONE 4.0 32B on your machine.

Run

lms load hf-lgai-exaone--exaone-4-0-32b-gguf && lms server start

Upgrade options

Hardware that runs EXAONE 4.0 32B well

MacBook Pro M4 Max 96GBBudget pick

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 285%.30.8 tok/s decode

Raises estimated decode speed by about 285%.

Adds memory headroom for longer context windows and future model growth.

~$2,499 MSRP

Mac Studio M3 Ultra 96GBBest value

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 256%.28.5 tok/s decode

Raises estimated decode speed by about 256%.

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run EXAONE 4.0 32B?

Yes, Mac mini M4 64GB can run EXAONE 4.0 32B with a C grade (Runs well). Expected decode speed: 8.0 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 31.1 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 mini M4 64GB?

On Mac mini M4 64GB, EXAONE 4.0 32B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24300ms using Q4_K_M quantization.

Can Mac mini M4 64GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Mac mini M4 64GB receives a C grade with 8.0 tok/s and 80K context.

What context window can EXAONE 4.0 32B use on Mac mini M4 64GB?

On Mac mini M4 64GB, EXAONE 4.0 32B can safely use up to 80K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for EXAONE 4.0 32B?

See all results for Mac mini M4 64GB See all hardware for EXAONE 4.0 32B

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