Can Nemotron Nano 9B v2 run on NVIDIA A2 16GB?

YES — Runs Great

A82Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~10.7 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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) — 10.7 GB, 30.5 tok/s, Runs well

10.7 GB required16.0 GB available

67% VRAM used

Fit status

Runs well

Decode

30.5 tok/s

TTFT

6338 ms

Safe context

51K

Memory

10.7 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on NVIDIA A2 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: 30.5 tok/s decode · 6.3s TTFT (warm) · 76 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	30.5 tok/s	3457 ms	51K
Coding	A	Runs well	30.5 tok/s	6338 ms	51K
Agentic Coding	A	Tight fit	30.5 tok/s	9219 ms	51K
Reasoning	A	Runs well	30.5 tok/s	7490 ms	51K
RAG	A	Tight fit	30.5 tok/s	11523 ms	51K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on NVIDIA A2 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 NVIDIA A2 16GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 14B	14B	S	19.7 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	18.7 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	17.4 tok/s
👁 Mistral Ministral 3 14B	14B	A	19.6 tok/s
👁 Mistral Codestral 2 25.08	22B	B	6.8 tok/s

Frequently asked questions

See all results for NVIDIA A2 16GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-a2-16gb

⇱ Nemotron Nano 9B v2 on NVIDIA A2 16GB? YES