Can EXAONE 4.0 32B run on RTX 3090 24GB?

BARELY — Tight on Memory

D39Poor

Estimated from fit model

EXAONE 4.0 32B needs ~26.9 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: HighStack: BasicBottleneck: Host offload

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) — 26.9 GB, 19.8 tok/s, Very compromised (needs ~2.1 GB host RAM)

26.9 GB required24.0 GB available

112% VRAM needed

2.9 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.1 GB host RAM)

Decode

19.8 tok/s

TTFT

9755 ms

Safe context

Memory

26.9 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB

KV Cache3.8 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on RTX 3090 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: 19.8 tok/s decode · 9.8s TTFT (warm) · 50 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.8 GB host RAM)	23.1 tok/s	4569 ms	4K
Coding	D	Very compromised (needs ~2.1 GB host RAM)	19.8 tok/s	9755 ms	4K
Agentic Coding	F	Too heavy	15.1 tok/s	18680 ms	4K
Reasoning	D	Very compromised (needs ~2.1 GB host RAM)	19.8 tok/s	11528 ms	4K
RAG	F	Too heavy	15.1 tok/s	23350 ms	4K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C50
Q3_K_S	3	15.7 GB	Low	C49
NVFP4Best for your GPU	4	17.9 GB	Medium	C49
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

Get started

Copy-paste commands to run EXAONE 4.0 32B on your machine.

Run

lms load hf-lgai-exaone--exaone-4-0-32b-gguf && lms server start

Upgrade options

Hardware that runs EXAONE 4.0 32B well

RTX 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+856)

Removes host-memory offload, which is usually the single biggest latency and throughput win.61.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 211%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.38.6 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 95%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.23.6 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Adds memory headroom for longer context windows and future model growth.

~$4,000 MSRP

Frequently asked questions

Can RTX 3090 24GB run EXAONE 4.0 32B?

Yes, RTX 3090 24GB can run EXAONE 4.0 32B with a D grade (Very compromised (needs ~2.1 GB host RAM)). Expected decode speed: 19.8 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 26.9 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 4.0 32B?

The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 4.0 32B run at on RTX 3090 24GB?

On RTX 3090 24GB, EXAONE 4.0 32B achieves approximately 19.8 tokens per second decode speed with a time-to-first-token of 9755ms using Q4_K_M quantization.

Can RTX 3090 24GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on RTX 3090 24GB receives a D grade with 19.8 tok/s and 4K context.

What context window can EXAONE 4.0 32B use on RTX 3090 24GB?

On RTX 3090 24GB, EXAONE 4.0 32B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if EXAONE 4.0 32B feels slow on RTX 3090 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 3090 24GB See all hardware for EXAONE 4.0 32B

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Preview:

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.8 GB host RAM)	23.1 tok/s	4569 ms	4K
Coding	D	Very compromised (needs ~2.1 GB host RAM)	19.8 tok/s	9755 ms	4K
Agentic Coding	F	Too heavy	15.1 tok/s	18680 ms	4K
Reasoning	D	Very compromised (needs ~2.1 GB host RAM)	19.8 tok/s	11528 ms	4K
RAG	F	Too heavy	15.1 tok/s	23350 ms	4K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	C50
Q3_K_S	3	15.7 GB	Low	C49
NVFP4Best for your GPU	4	17.9 GB	Medium	C49
Q4_K_M	4	19.5 GB	Medium	F0
Q5_K_M	5	23.0 GB	High	F0
Q6_K	6	26.2 GB	High	F0
Q8_0	8	34.2 GB	Very High	F0
F16	16	65.6 GB	Maximum	F0

Frequently asked questions

Can RTX 3090 24GB run EXAONE 4.0 32B?

Yes, RTX 3090 24GB can run EXAONE 4.0 32B with a D grade (Very compromised (needs ~2.1 GB host RAM)). Expected decode speed: 19.8 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 26.9 GB of memory with Q4_K_M quantization.

What is the best quantization for EXAONE 4.0 32B?

The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.

What speed will EXAONE 4.0 32B run at on RTX 3090 24GB?

On RTX 3090 24GB, EXAONE 4.0 32B achieves approximately 19.8 tokens per second decode speed with a time-to-first-token of 9755ms using Q4_K_M quantization.

Can RTX 3090 24GB run EXAONE 4.0 32B for coding?

For coding workloads, EXAONE 4.0 32B on RTX 3090 24GB receives a D grade with 19.8 tok/s and 4K context.

What context window can EXAONE 4.0 32B use on RTX 3090 24GB?

On RTX 3090 24GB, EXAONE 4.0 32B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if EXAONE 4.0 32B feels slow on RTX 3090 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.