Can EXAONE 3.5 2.4B Instruct run on RTX 5090 Laptop 24GB?

YES — Runs Great

C43Usable

Estimated from fit model

EXAONE 3.5 2.4B Instruct needs ~5.3 GB VRAM. RTX 5090 Laptop 24GB has 24.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 5.3 GB, 33.6 tok/s, Runs well

5.3 GB required24.0 GB available

22% VRAM used

Fit status

Runs well

Decode

33.6 tok/s

TTFT

5762 ms

Safe context

1.1M

Memory

5.3 GB / 24.0 GB

Memory breakdown

Weights1.5 GB

KV Cache0.3 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsEXAONE 3.5 2.4B Instruct on RTX 5090 Laptop 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: 33.6 tok/s decode · 5.8s TTFT (warm) · 84 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	33.6 tok/s	3143 ms	1.1M
Coding	C	Runs well	33.6 tok/s	5762 ms	1.1M
Agentic Coding	C	Runs well	33.6 tok/s	8381 ms	1.1M
Reasoning	C	Runs well	33.6 tok/s	6810 ms	1.1M
RAG	C	Runs well	33.6 tok/s	10476 ms	1.1M

Quantization options

How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.9 GB	Low	C43
Q3_K_S	3	1.2 GB	Low	C44
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 Pro 36GBBudget pick

36 GB Unified (+12)

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

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+12)

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

~$2,499 MSRP

Frequently asked questions

Can RTX 5090 Laptop 24GB run EXAONE 3.5 2.4B Instruct?

Yes, RTX 5090 Laptop 24GB 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 5.3 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 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, 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 5090 Laptop 24GB run EXAONE 3.5 2.4B Instruct for coding?

For coding workloads, EXAONE 3.5 2.4B Instruct on RTX 5090 Laptop 24GB receives a C grade with 33.6 tok/s and 1.1M context.

What context window can EXAONE 3.5 2.4B Instruct use on RTX 5090 Laptop 24GB?

On RTX 5090 Laptop 24GB, EXAONE 3.5 2.4B Instruct can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 5090 Laptop 24GB See all hardware for EXAONE 3.5 2.4B Instruct

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