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

Q: 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.

BARELY — Tight on Memory

A74Great

Estimated from fit model

EXAONE 4.0 32B needs ~27.0 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) — 27.0 GB, 21.2 tok/s, Very compromised (needs ~2.2 GB host RAM)

27.0 GB required24.0 GB available

113% VRAM needed

3.0 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.2 GB host RAM)

Decode

21.2 tok/s

TTFT

9143 ms

Safe context

Memory

27.0 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights19.5 GB

KV Cache3.9 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
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 21.2 tok/s decode · 9.1s TTFT (warm) · 53 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 {ram} 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	A	Runs with offload (needs ~0.8 GB host RAM)	24.8 tok/s	4259 ms	4K
Coding	A	Very compromised	19.6 tok/s	9874 ms	4K
Agentic Coding	F	Too heavy	15.9 tok/s	17670 ms	4K
Reasoning	A	Very compromised (needs ~2.2 GB host RAM)	21.2 tok/s	10805 ms	4K
RAG	F	Too heavy	15.9 tok/s	22087 ms

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	S85
Q3_K_S	3	15.7 GB	Low	A85
NVFP4Best for your GPU

Get started

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

Run

ollama run exaone-4:32b

Your hardware

More models your RTX 3090 24GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 35B A3B	35B	A	55.5 tok/s

Frequently asked questions

Can RTX 3090 24GB run EXAONE 4.0 32B?

Yes, RTX 3090 24GB can run EXAONE 4.0 32B with a A grade (Very compromised). Expected decode speed: 19.6 tok/s.

How much VRAM does EXAONE 4.0 32B need?

EXAONE 4.0 32B (32B parameters) requires approximately 27.0 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 19.6 tokens per second decode speed with a time-to-first-token of 9874ms 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 A grade with 19.6 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 131K, but available memory constrains the safe maximum.

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

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

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