Can EXAONE 4.0 32B run on NVIDIA L4 24GB?

BARELY — Tight on Memory

A70Great

○Estimated from fit model

EXAONE 4.0 32B needs ~27.0 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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, 6.3 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

6.3 tok/s

TTFT

30721 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 NVIDIA L4 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: 6.3 tok/s decode · 30.7s TTFT (warm) · 16 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	6.8 tok/s	15454 ms	4K
Coding	A	Very compromised	5.8 tok/s	33178 ms	4K
Agentic Coding	F	Too heavy	4.7 tok/s	59371 ms	4K
Reasoning	A	Very compromised (needs ~2.2 GB host RAM)	6.3 tok/s	36306 ms	4K
RAG	F	Too heavy	4.4 tok/s	80150 ms	4K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on NVIDIA L4 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 NVIDIA L4 24GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.5 35B A3B	35B	A	17.7 tok/s

Frequently asked questions

See all results for NVIDIA L4 24GB See all hardware for EXAONE 4.0 32B

URL: https://willitrunai.com/can-run/exaone-4-32b-on-l4-24gb

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