Will It Run AI

Can EXAONE 4.0 32B run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

C52Usable
Estimated from fit model

EXAONE 4.0 32B needs ~31.1 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~24 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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, 23.8 tok/s, Runs well
31.1 GB required46.1 GB available
67% VRAM used

Fit status

Runs well

Decode

23.8 tok/s

TTFT

8145 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 Studio M2 Ultra 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: 23.8 tok/s decode · 8.1s TTFT (warm) · 59 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well23.8 tok/s4442 ms80K
CodingCRuns well23.8 tok/s8145 ms80K
Agentic CodingCRuns well23.8 tok/s11847 ms80K
ReasoningCRuns well23.8 tok/s9625 ms80K
RAGCRuns well23.8 tok/s14808 ms80K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
12.5 GB
LowC44
Q3_K_S
3
15.7 GB
LowC45
NVFP4
4
17.9 GB
MediumC46
Q4_K_M
4
19.5 GB
MediumC46
Q5_K_M
5
23.0 GB
HighC48
Q6_K
6
26.2 GB
HighC48
Q8_0Best for your GPU
8
34.2 GB
Very HighC48
F16
16
65.6 GB
MaximumF0

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

Opções de upgrade

Hardware que roda bem EXAONE 4.0 32B

NVIDIARTX PRO 5000 Blackwell 48GBOpção econômica
1344 GB/s (+544)
C
Raises estimated decode speed by about 143%.57.8 tok/s decodificação

Raises estimated decode speed by about 143%.

~$4,999 MSRP

NVIDIARTX 6000 Ada 48GBMelhor custo-benefício
960 GB/s (+160)
C
Raises estimated decode speed by about 69%.40.3 tok/s decodificação

Raises estimated decode speed by about 69%.

~$6,800 MSRP

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run EXAONE 4.0 32B?

Yes, Mac Studio M2 Ultra 64GB can run EXAONE 4.0 32B with a C grade (Runs well). Expected decode speed: 23.8 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 Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, EXAONE 4.0 32B achieves approximately 23.8 tokens per second decode speed with a time-to-first-token of 8145ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on Mac Studio M2 Ultra 64GB receives a C grade with 23.8 tok/s and 80K context.

What context window can EXAONE 4.0 32B use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 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 Studio M2 Ultra 64GB as fast as VRAM for EXAONE 4.0 32B?

Not always. Mac Studio M2 Ultra 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.

See all results for Mac Studio M2 Ultra 64GBSee all hardware for EXAONE 4.0 32B
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