Can EXAONE 4.0 32B run on RTX 5000 Ada 32GB?

YES — Tight Fit

A85Great

○Estimated from fit model

EXAONE 4.0 32B needs ~27.8 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 27.8 GB, 25.5 tok/s, Tight fit

27.8 GB required32.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

25.5 tok/s

TTFT

7594 ms

Safe context

33K

Memory

27.8 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on RTX 5000 Ada 32GB
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: 25.5 tok/s decode · 7.6s TTFT (warm) · 64 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	S	Runs well	25.5 tok/s	4142 ms	33K
Coding	A	Tight fit	25.5 tok/s	7594 ms	33K
Agentic Coding	A	Runs with offload	25.5 tok/s	11045 ms	33K
Reasoning	A	Tight fit	25.5 tok/s	8974 ms	33K
RAG	A	Runs with offload	25.5 tok/s	13807 ms	33K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	12.5 GB	Low	A83
Q3_K_S	3	15.7 GB	Low	A84
NVFP4	4	17.9 GB	Medium	A84
Q4_K_M	4	19.5 GB	Medium	A84
Q5_K_MBest for your GPU	5	23.0 GB	High	A84
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

ollama run exaone-4:32b

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	58.6 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	63.7 tok/s

Frequently asked questions

See all results for RTX 5000 Ada 32GB See all hardware for EXAONE 4.0 32B

URL: https://willitrunai.com/can-run/exaone-4-32b-on-rtx-5000-ada-32gb

⇱ EXAONE 4.0 32B on RTX 5000 Ada 32GB? TIGHT FIT