Can Gemma 3 12B run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A81Great

○Estimated from fit model

Gemma 3 12B needs ~16.6 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~54 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 16.6 GB, 54.2 tok/s, Runs well

16.6 GB required32.0 GB available

52% VRAM used

Fit status

Runs well

Decode

54.2 tok/s

TTFT

3574 ms

Safe context

66K

Memory

16.6 GB / 32.0 GB

Memory breakdown

Weights7.3 GB

KV Cache4.9 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsGemma 3 12B 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: 54.2 tok/s decode · 3.6s TTFT (warm) · 135 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	A	Runs well	54.2 tok/s	1950 ms	66K
Coding	A	Runs well	54.2 tok/s	3574 ms	66K
Agentic Coding	A	Runs well	54.2 tok/s	5199 ms	66K
Reasoning	A	Runs well	54.2 tok/s	4224 ms	66K
RAG	A	Runs well	54.2 tok/s	6499 ms	66K

Quantization options

How Gemma 3 12B (12B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A73
Q3_K_S	3	5.9 GB	Low	A74
NVFP4	4	6.7 GB	Medium	A74
Q4_K_M	4	7.3 GB	Medium	A74
Q5_K_M	5	8.6 GB	High	A75
Q6_K	6	9.8 GB	High	A75
Q8_0	8	12.8 GB	Very High	A77
F16Best for your GPU	16	24.6 GB	Maximum	A78

Get started

Copy-paste commands to run Gemma 3 12B on your machine.

Run

ollama run gemma3:12b

Your hardware

More models your Radeon AI PRO R9700 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	57.1 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	24.8 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	24.8 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	48 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	59.1 tok/s

Frequently asked questions

See all results for Radeon AI PRO R9700 32GB See all hardware for Gemma 3 12B

URL: https://willitrunai.com/can-run/gemma-3-12b-on-radeon-ai-pro-r9700-32gb

⇱ Gemma 3 12B on Radeon AI PRO R9700 32GB? YES