Can Nemotron Nano 9B v2 run on RTX PRO 4000 Blackwell 24GB?

YES — Runs Great

A82Great

○Estimated from fit model

Nemotron Nano 9B v2 needs ~11.5 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~111 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) — 11.5 GB, 110.5 tok/s, Runs well

11.5 GB required24.0 GB available

48% VRAM used

Fit status

Runs well

Decode

110.5 tok/s

TTFT

1752 ms

Safe context

98K

Memory

11.5 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on RTX PRO 4000 Blackwell 24GB
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: 110.5 tok/s decode · 1.8s TTFT (warm) · 276 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	110.5 tok/s	955 ms	98K
Coding	A	Runs well	110.5 tok/s	1752 ms	98K
Agentic Coding	A	Runs well	110.5 tok/s	2548 ms	98K
Reasoning	A	Runs well	110.5 tok/s	2070 ms	98K
RAG	A	Runs well	110.5 tok/s	3185 ms	98K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A74
Q3_K_S	3	4.4 GB	Low	A75
NVFP4	4	5.0 GB	Medium	A75
Q4_K_M	4	5.5 GB	Medium	A75
Q5_K_M	5	6.5 GB	High	A76
Q6_K	6	7.4 GB	High	A76
Q8_0	8	9.6 GB	Very High	A78
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 RTX PRO 4000 Blackwell 24GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	85.4 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	37 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	37.1 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	88.3 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	A	49.1 tok/s

Frequently asked questions

See all results for RTX PRO 4000 Blackwell 24GB See all hardware for Nemotron Nano 9B v2

URL: https://willitrunai.com/can-run/nemotron-nano-9b-v2-on-rtx-pro-4000-blackwell-24gb

⇱ Nemotron Nano 9B v2 on RTX PRO 4000 Blackwell 24GB? YES