Can Nemotron Nano 9B v2 run on Radeon AI PRO R9700 32GB?

YES — Runs Great

A79Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~12.0 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~74 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 12.0 GB, 73.9 tok/s, Runs well

12.0 GB required32.0 GB available

38% VRAM used

Fit status

Runs well

Decode

73.9 tok/s

TTFT

2618 ms

Safe context

131K

Memory

12.0 GB / 32.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 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: 73.9 tok/s decode · 2.6s TTFT (warm) · 185 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	73.9 tok/s	1428 ms	131K
Coding	A	Runs well	73.9 tok/s	2618 ms	131K
Agentic Coding	A	Runs well	73.9 tok/s	3809 ms	131K
Reasoning	A	Runs well	73.9 tok/s	3094 ms	131K
RAG	A	Runs well	73.9 tok/s	4761 ms	131K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A73
Q3_K_S	3	4.4 GB	Low	A73
NVFP4	4	5.0 GB	Medium	A73
Q4_K_M	4	5.5 GB	Medium	A73
Q5_K_M	5	6.5 GB	High	A74
Q6_K	6	7.4 GB	High	A74
Q8_0	8	9.6 GB	Very High	A75
F16Best for your GPU	16	18.5 GB	Maximum	A79

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

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	18.8 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 Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-radeon-ai-pro-r9700-32gb

⇱ Nemotron Nano 9B v2 on Radeon AI PRO R9700 32GB? YES