Can EXAONE 3.5 2.4B Instruct run on RTX 4090 Laptop 16GB?

YES — Runs Great

C44Usable

Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~4.2 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 4.2 GB, 38.4 tok/s, Runs well

4.2 GB required16.0 GB available

26% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5042 ms

Safe context

685K

Memory

4.2 GB / 16.0 GB

Memory breakdown

Weights1.5 GB

KV Cache0.3 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 2.4B Instruct on RTX 4090 Laptop 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	38.4 tok/s	2750 ms	685K
Coding	C	Runs well	33.6 tok/s	5762 ms	685K
Agentic Coding	C	Runs well	38.4 tok/s	7333 ms	685K
Reasoning	C	Runs well	38.4 tok/s	5958 ms	685K
RAG	C	Runs well	38.4 tok/s	9167 ms	685K

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.9 GB	Low	C45
Q3_K_S	3	1.2 GB	Low	C45
NVFP4	4

Get started

Copy-paste commands to run EXAONE 3.5 2.4B Instruct on your machine.

Run

lms load hf-lmstudio-community--exaone-3-5-2-4b-instruct-gguf && lms server start

Upgrade options

Hardware that runs EXAONE 3.5 2.4B Instruct well

MacBook Pro M3 24GBBudget pick

24 GB Unified (+8)

This setup is broadly balanced for this model.33.6 tok/s decode

~$1,099 MSRP

MacBook Air M3 24GBBest value

24 GB Unified (+8)

This setup is broadly balanced for this model.33.6 tok/s decode

~$1,099 MSRP

Frequently asked questions

Can RTX 4090 Laptop 16GB run EXAONE 3.5 2.4B Instruct?

Yes, RTX 4090 Laptop 16GB can run EXAONE 3.5 2.4B Instruct with a C grade (Runs well). Expected decode speed: 33.6 tok/s.

How much VRAM does EXAONE 3.5 2.4B Instruct need?

EXAONE 3.5 2.4B Instruct (2.4000000953674316B parameters) requires approximately 4.2 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 3.5 2.4B Instruct?

The recommended quantization for EXAONE 3.5 2.4B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 3.5 2.4B Instruct run at on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, EXAONE 3.5 2.4B Instruct achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5762ms using Q4_K_M quantization.

Can RTX 4090 Laptop 16GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on RTX 4090 Laptop 16GB receives a C grade with 33.6 tok/s and 685K context.

What context window can EXAONE 3.5 2.4B Instruct use on RTX 4090 Laptop 16GB?

On RTX 4090 Laptop 16GB, EXAONE 3.5 2.4B Instruct can safely use up to 685K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4090 Laptop 16GB See all hardware for EXAONE 3.5 2.4B Instruct

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