Will It Run AI

Can EXAONE 3.5 7.8B Instruct i1 run on MacBook Pro M2 Max 32GB?

YES — Runs Great

C49Usable
Estimated from fit model

EXAONE 3.5 7.8B Instruct i1 needs ~10.0 GB VRAM. MacBook Pro M2 Max 32GB has 23.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 10.0 GB, 48.8 tok/s, Runs well
10.0 GB required23.0 GB available
43% VRAM used

Fit status

Runs well

Decode

48.8 tok/s

TTFT

3970 ms

Safe context

244K

Memory

10.0 GB / 23.0 GB

Memory breakdown

Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom3.5 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 7.8B Instruct i1 on MacBook Pro M2 Max 32GB
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: 48.8 tok/s decode · 4.0s TTFT (warm) · 122 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 well48.8 tok/s2166 ms244K
CodingCRuns well48.8 tok/s3970 ms244K
Agentic CodingCRuns well48.8 tok/s5775 ms244K
ReasoningCRuns well48.8 tok/s4692 ms244K
RAGCRuns well48.8 tok/s7219 ms244K

Quantization options

How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on MacBook Pro M2 Max 32GB (23.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.0 GB
LowC44
Q3_K_S
3
3.8 GB
LowC45
NVFP4
4
4.4 GB
MediumC45
Q4_K_M
4
4.8 GB
MediumC45
Q5_K_M
5
5.6 GB
HighC46
Q6_K
6
6.4 GB
HighC46
Q8_0
8
8.3 GB
Very HighC48
F16Best for your GPU
16
16.0 GB
MaximumC50

Get started

Copy-paste commands to run EXAONE 3.5 7.8B Instruct i1 on your machine.

Run

lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server start

Opções de upgrade

Hardware que roda bem EXAONE 3.5 7.8B Instruct i1

RX 7900 XTX 24GBOpção econômica
960 GB/s (+560)
C
Raises estimated decode speed by about 124%.109.2 tok/s decodificação

Raises estimated decode speed by about 124%.

~$999 MSRP

NVIDIARTX 3090 24GBMelhor custo-benefício
936 GB/s (+536)
C
Raises estimated decode speed by about 92%.93.6 tok/s decodificação

Raises estimated decode speed by about 92%.

~$1,499 MSRP

Frequently asked questions

Can MacBook Pro M2 Max 32GB run EXAONE 3.5 7.8B Instruct i1?

Yes, MacBook Pro M2 Max 32GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 48.8 tok/s.

How much VRAM does EXAONE 3.5 7.8B Instruct i1 need?

EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 7.8B Instruct i1?

The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 7.8B Instruct i1 run at on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 48.8 tokens per second decode speed with a time-to-first-token of 3970ms using Q4_K_M quantization.

Can MacBook Pro M2 Max 32GB run EXAONE 3.5 7.8B Instruct i1 for coding?

For coding workloads, EXAONE 3.5 7.8B Instruct i1 on MacBook Pro M2 Max 32GB receives a C grade with 48.8 tok/s and 244K context.

What context window can EXAONE 3.5 7.8B Instruct i1 use on MacBook Pro M2 Max 32GB?

On MacBook Pro M2 Max 32GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 244K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Max 32GB as fast as VRAM for EXAONE 3.5 7.8B Instruct i1?

Not always. MacBook Pro M2 Max 32GB 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 M2 Max 32GBSee all hardware for EXAONE 3.5 7.8B Instruct i1
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