Can Nemotron Nano 9B v2 run on RX 9070 XT 16GB?

YES — Runs Great

A85Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~10.4 GB VRAM. RX 9070 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 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) — 10.4 GB, 80.2 tok/s, Runs well

10.4 GB required16.0 GB available

65% VRAM used

Fit status

Runs well

Decode

80.2 tok/s

TTFT

2414 ms

Safe context

52K

Memory

10.4 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on RX 9070 XT 16GB
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: 80.2 tok/s decode · 2.4s TTFT (warm) · 201 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	80.2 tok/s	1317 ms	52K
Coding	A	Runs well	80.2 tok/s	2414 ms	52K
Agentic Coding	S	Runs well	80.2 tok/s	3511 ms	52K
Reasoning	A	Runs well	80.2 tok/s	2853 ms	52K
RAG	S	Runs well	80.2 tok/s	4389 ms	52K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RX 9070 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A77
Q3_K_S	3	4.4 GB	Low	A78
NVFP4	4	5.0 GB	Medium	A78
Q4_K_M	4	5.5 GB	Medium	A79
Q5_K_M	5	6.5 GB	High	A80
Q6_K	6	7.4 GB	High	A81
Q8_0Best for your GPU	8	9.6 GB	Very High	A81
F16	16	18.5 GB	Maximum	F0

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 RX 9070 XT 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	S	51.8 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	49.1 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	48.6 tok/s
👁 Mistral Ministral 3 14B	14B	S	51.6 tok/s
👁 Mistral Codestral 2 25.08	22B	A	17.8 tok/s

Frequently asked questions

See all results for RX 9070 XT 16GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-rx-9070-xt-16gb

⇱ Nemotron Nano 9B v2 on RX 9070 XT 16GB? YES