text model · EXAONE · Windows

Can I run EXAONE 4.0 32B on 16GB RAM Laptop (CPU/iGPU only)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. EXAONE 4.0 32B needs ~20.2 GB even at Q4_K_M, but 16GB RAM Laptop (CPU/iGPU only) only has ~12 GB usable.

Needs ~20.2 GB Device usable ~12 GB

Needs ~20.2 GB even at Q4_K_M, but only ~12 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 8.2 GB: EXAONE 4.0 32B needs roughly 20.2 GB at Q4_K_M and 16GB RAM Laptop (CPU/iGPU only) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs EXAONE 4.0 32B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See EXAONE 4.0 32B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~20.2 GB
Usable on device: ~12 GB
Device memory: 16 GB

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Which quant fits

Quant ladder vs ~12 GB usable

Q2_K

~15.6 GB

Q3_K_M

~17.8 GB

Q4_K_M

~20.2 GB

Q5_K_M

~25 GB

Q6_K

~28.4 GB

Q8_0

~33.9 GB

FP16

~66.2 GB

The line marks 16GB RAM Laptop (CPU/iGPU only)'s ~12 GB budget; rungs past it are too large.

How to run it

On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).

Model EXAONE

Parameters: 32B
Q4_K_M size: 18.02 GB
Q8_0 size: 31.67 GB
Context: 131k

Full EXAONE 4.0 32B requirements →

Device Windows

Memory: 16 GB ram
Usable for weights: ~12 GB
Power draw: ~28 W
Best runtime: Ollama (llama.cpp backend)

Best models for 16GB RAM Laptop (CPU/iGPU only) →

What you can run instead

Run EXAONE 4.0 32B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M5 (32GB)

FAQ

Can 16GB RAM Laptop (CPU/iGPU only) run EXAONE 4.0 32B?

No. EXAONE 4.0 32B needs ~20.2 GB even at Q4_K_M, but 16GB RAM Laptop (CPU/iGPU only) only has ~12 GB usable.

How much memory does EXAONE 4.0 32B need?

16GB RAM Laptop (CPU/iGPU only) does not have enough memory. At Q4_K_M the weights are ~18.02 GB; with KV cache and runtime overhead, budget ~20.2 GB at a 4k context.

What is the best tool to run EXAONE 4.0 32B on Windows?

LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.

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Markdown

[![EXAONE 4.0 32B on 16GB RAM Laptop (CPU/iGPU only)](https://localmodel.run/badge/exaone-4-32b/laptop-16gb.svg)](https://localmodel.run/can-i-run/exaone-4-32b/laptop-16gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.