Can EXAONE 4.0 32B run on Radeon AI PRO R9700 32GB?

YES — Tight Fit

A84Great

○Estimated from fit model

EXAONE 4.0 32B needs ~27.5 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 27.5 GB, 20.9 tok/s, Tight fit

27.5 GB required32.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

20.9 tok/s

TTFT

9267 ms

Safe context

34K

Memory

27.5 GB / 32.0 GB

Memory breakdown

Weights19.5 GB

KV Cache3.9 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsEXAONE 4.0 32B on Radeon AI PRO R9700 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: 20.9 tok/s decode · 9.3s TTFT (warm) · 52 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	20.9 tok/s	5055 ms	34K
Coding	A	Tight fit	20.9 tok/s	9267 ms	34K
Agentic Coding	A	Runs with offload	20.9 tok/s	13479 ms	34K
Reasoning	A	Tight fit	20.9 tok/s	10952 ms	34K
RAG	A	Runs with offload	20.9 tok/s	16849 ms	34K

Quantization options

How EXAONE 4.0 32B (32B params) fits at each quantization level on Radeon AI PRO R9700 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 Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode	Capabilities
👁 Alibaba Qwen 3.6 35B A3B	35B	S	48 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	52.2 tok/s

Frequently asked questions

See all results for Radeon AI PRO R9700 32GB See all hardware for EXAONE 4.0 32B

URL: https://willitrunai.com/can-run/exaone-4-32b-on-radeon-ai-pro-r9700-32gb

⇱ EXAONE 4.0 32B on Radeon AI PRO R9700 32GB? TIGHT FIT