Can EXAONE 4.0 32B run on Mac Studio M3 Ultra 96GB?

Q: Is unified memory on Mac Studio M3 Ultra 96GB as fast as VRAM for EXAONE 4.0 32B?

Not always. Mac Studio M3 Ultra 96GB 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

C49Usable

Estimated from fit model

EXAONE 4.0 32B needs ~34.5 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~29 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) — 34.5 GB, 28.5 tok/s, Runs well

34.5 GB required69.1 GB available

50% VRAM used

Fit status

Runs well

Decode

28.5 tok/s

TTFT

6786 ms

Safe context

164K

Memory

34.5 GB / 69.1 GB

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on Mac Studio M3 Ultra 96GB
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: 28.5 tok/s decode · 6.8s TTFT (warm) · 71 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	28.5 tok/s	3701 ms	164K
Coding	C	Runs well	28.5 tok/s	6786 ms	164K
Agentic Coding	C	Runs well	28.5 tok/s	9870 ms	164K
Reasoning	C	Runs well	28.5 tok/s	8019 ms	164K
RAG	C	Runs well	28.5 tok/s	12338 ms	164K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C41
Q3_K_S	3	15.7 GB	Low	C42
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

RTX PRO 6000 Blackwell Workstation Edition 96GBBudget pick

1792 GB/s (+973)

Raises estimated decode speed by about 171%.77.1 tok/s decode

Raises estimated decode speed by about 171%.

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

~$9,999 MSRP

NVIDIA H20 96GBBest value

4000 GB/s (+3181)

Raises estimated decode speed by about 482%.166 tok/s decode

Raises estimated decode speed by about 482%.

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

~$12,000 MSRP

Frequently asked questions

Can Mac Studio M3 Ultra 96GB run EXAONE 4.0 32B?

Yes, Mac Studio M3 Ultra 96GB can run EXAONE 4.0 32B with a C grade (Runs well). Expected decode speed: 28.5 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 34.5 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 Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, EXAONE 4.0 32B achieves approximately 28.5 tokens per second decode speed with a time-to-first-token of 6786ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 96GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Mac Studio M3 Ultra 96GB receives a C grade with 28.5 tok/s and 164K context.

What context window can EXAONE 4.0 32B use on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, EXAONE 4.0 32B can safely use up to 164K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 96GB as fast as VRAM for EXAONE 4.0 32B?

See all results for Mac Studio M3 Ultra 96GB See all hardware for EXAONE 4.0 32B

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