Is unified memory on MacBook Air M2 16GB as fast as VRAM for EXAONE 3.5 7.8B Instruct?

Not always. MacBook Air M2 16GB 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.

Can EXAONE 3.5 7.8B Instruct run on MacBook Air M2 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

EXAONE 3.5 7.8B Instruct needs ~8.3 GB VRAM. MacBook Air M2 16GB has 11.5 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) — 8.3 GB, 13.7 tok/s, Runs well

8.3 GB required11.5 GB available

72% VRAM used

Fit status

Runs well

Decode

13.7 tok/s

TTFT

14172 ms

Safe context

72K

Memory

8.3 GB / 11.5 GB

Memory breakdown

Weights4.8 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct on MacBook Air M2 16GB
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: 13.7 tok/s decode · 14.2s TTFT (warm) · 34 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	13.7 tok/s	7730 ms	72K
Coding	C	Runs well	13.7 tok/s	14172 ms	72K
Agentic Coding	C	Runs well	13.7 tok/s	20613 ms	72K
Reasoning	C	Runs well	13.7 tok/s	16748 ms	72K
RAG	C	Runs well	13.7 tok/s	25766 ms	72K

Quantization options

How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.0 GB	Low	C49
Q3_K_S	3	3.8 GB	Low	C51
NVFP4	4

Get started

Copy-paste commands to run EXAONE 3.5 7.8B Instruct on your machine.

Run

lms load hf-lgai-exaone--exaone-3-5-7-8b-instruct-gguf && lms server start

Upgrade options

Hardware that runs EXAONE 3.5 7.8B Instruct well

Intel Arc B580 12GBBest value

456 GB/s (+356)

Raises estimated decode speed by about 236%.46 tok/s decode

Raises estimated decode speed by about 236%.

~$249 MSRP

MacBook Pro M3 Pro 18GBBudget pick

18 GB Unified (+2)150 GB/s (+50)

Raises estimated decode speed by about 68%.23 tok/s decode

Raises estimated decode speed by about 68%.

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M2 16GB run EXAONE 3.5 7.8B Instruct?

Yes, MacBook Air M2 16GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 13.7 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct need?

EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 7.8B Instruct?

The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct run at on MacBook Air M2 16GB?

On MacBook Air M2 16GB, EXAONE 3.5 7.8B Instruct achieves approximately 13.7 tokens per second decode speed with a time-to-first-token of 14172ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run EXAONE 3.5 7.8B Instruct for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct on MacBook Air M2 16GB receives a C grade with 13.7 tok/s and 72K context.

What context window can EXAONE 3.5 7.8B Instruct use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, EXAONE 3.5 7.8B Instruct can safely use up to 72K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Air M2 16GB See all hardware for EXAONE 3.5 7.8B Instruct

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