Can exaone 3.0 7.8b it run on MacBook Pro M3 Max 128GB?

YES — Runs Great

C44Usable

Estimated from fit model

exaone 3.0 7.8b it needs ~20.4 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~50 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 20.4 GB, 50.4 tok/s, Runs well

20.4 GB required92.2 GB available

22% VRAM used

Fit status

Runs well

Decode

50.4 tok/s

TTFT

3838 ms

Safe context

1.3M

Memory

20.4 GB / 92.2 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsexaone 3.0 7.8b it on MacBook Pro M3 Max 128GB
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: 50.4 tok/s decode · 3.8s TTFT (warm) · 126 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	50.4 tok/s	2094 ms	1.3M
Coding	C	Runs well	50.4 tok/s	3838 ms	1.3M
Agentic Coding	C	Runs well	50.4 tok/s	5583 ms	1.3M
Reasoning	C	Runs well	50.4 tok/s	4536 ms	1.3M
RAG	C	Runs well	50.4 tok/s	6978 ms	1.3M

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	D39
Q3_K_S	3	3.8 GB	Low	D39
NVFP4	4	4.4 GB	Medium	D39
Q4_K_M	4	4.8 GB	Medium	D39
Q5_K_M	5	5.6 GB	High	D39
Q6_K	6	6.4 GB	High	D39
Q8_0	8	8.3 GB	Very High	D39
F16Best for your GPU	16	16.0 GB	Maximum	D40

Get started

Copy-paste commands to run exaone 3.0 7.8b it on your machine.

Run

lms load hf-bingsu--exaone-3-0-7-8b-it && lms server start

アップグレードオプション

exaone 3.0 7.8b itを快適に動かすハードウェア

Mac Studio M3 Ultra 256GBコスパ重視

256 GB Unified (+128)819 GB/s (+419)

Raises estimated decode speed by about 117%.109.2 tok/sデコード

Raises estimated decode speed by about 117%.

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

〜$6,999 MSRP

RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ最良

1792 GB/s (+1392)

Raises estimated decode speed by about 117%.109.2 tok/sデコード

Raises estimated decode speed by about 117%.

〜$9,999 MSRP

Frequently asked questions

Can MacBook Pro M3 Max 128GB run exaone 3.0 7.8b it?

Yes, MacBook Pro M3 Max 128GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 50.4 tok/s.

How much VRAM does exaone 3.0 7.8b it need?

exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.

What is the best quantization for exaone 3.0 7.8b it?

The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.

What speed will exaone 3.0 7.8b it run at on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, exaone 3.0 7.8b it achieves approximately 50.4 tokens per second decode speed with a time-to-first-token of 3838ms using Q4_K_M quantization.

Can MacBook Pro M3 Max 128GB run exaone 3.0 7.8b it for coding?

For coding workloads, exaone 3.0 7.8b it on MacBook Pro M3 Max 128GB receives a C grade with 50.4 tok/s and 1.3M context.

What context window can exaone 3.0 7.8b it use on MacBook Pro M3 Max 128GB?

On MacBook Pro M3 Max 128GB, exaone 3.0 7.8b it can safely use up to 1.3M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Max 128GB as fast as VRAM for exaone 3.0 7.8b it?

Not always. MacBook Pro M3 Max 128GB 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 128GB See all hardware for exaone 3.0 7.8b it

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