Can exaone 3.0 7.8b it run on MacBook Pro M3 24GB?

Q: Is unified memory on MacBook Pro M3 24GB as fast as VRAM for exaone 3.0 7.8b it?

Not always. MacBook Pro M3 24GB 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

C47Usable

Estimated from fit model

exaone 3.0 7.8b it needs ~9.2 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~14 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) — 9.2 GB, 14.3 tok/s, Runs well

9.2 GB required17.3 GB available

53% VRAM used

Fit status

Runs well

Decode

14.3 tok/s

TTFT

13546 ms

Safe context

158K

Memory

9.2 GB / 17.3 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsexaone 3.0 7.8b it on MacBook Pro M3 24GB
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: 14.3 tok/s decode · 13.5s TTFT (warm) · 36 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	14.3 tok/s	7389 ms	158K
Coding	C	Runs well	14.3 tok/s	13546 ms	158K
Agentic Coding	C	Runs well	14.3 tok/s	19704 ms	158K
Reasoning	C	Runs well	14.3 tok/s	16009 ms	158K
RAG	C	Runs well	14.3 tok/s	24630 ms	158K

Quantization options

How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C46
Q3_K_S	3	3.8 GB	Low	C47
NVFP4	4

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

Upgrade options

Hardware that runs exaone 3.0 7.8b it well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 241%.48.8 tok/s decode

Raises estimated decode speed by about 241%.

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

~$1,999 MSRP

MacBook Pro M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 106%.29.4 tok/s decode

Raises estimated decode speed by about 106%.

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

~$1,999 MSRP

MacBook Pro M4 Max 36GBApple upgrade

36 GB Unified (+12)410 GB/s (+310)

Raises estimated decode speed by about 314%.59.2 tok/s decode

Raises estimated decode speed by about 314%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M3 24GB run exaone 3.0 7.8b it?

Yes, MacBook Pro M3 24GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 14.3 tok/s.

How much VRAM does exaone 3.0 7.8b it need?

exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 9.2 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 24GB?

On MacBook Pro M3 24GB, exaone 3.0 7.8b it achieves approximately 14.3 tokens per second decode speed with a time-to-first-token of 13546ms using Q4_K_M quantization.

Can MacBook Pro M3 24GB run exaone 3.0 7.8b it for coding?

For coding workloads, exaone 3.0 7.8b it on MacBook Pro M3 24GB receives a C grade with 14.3 tok/s and 158K context.

What context window can exaone 3.0 7.8b it use on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, exaone 3.0 7.8b it can safely use up to 158K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 24GB as fast as VRAM for exaone 3.0 7.8b it?

See all results for MacBook Pro M3 24GB See all hardware for exaone 3.0 7.8b it

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