Can Nemotron 3 Nano 30B run on RTX 5000 Ada 32GB?

YES — Runs Great

S93Excellent

○Estimated from fit model

Nemotron 3 Nano 30B needs ~25.1 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~27 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) — 25.1 GB, 27.1 tok/s, Runs well

25.1 GB required32.0 GB available

78% VRAM used

Fit status

Runs well

Decode

27.1 tok/s

TTFT

7152 ms

Safe context

61K

Memory

25.1 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on RTX 5000 Ada 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: 27.1 tok/s decode · 7.2s TTFT (warm) · 68 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	S	Runs well	27.1 tok/s	3901 ms	61K
Coding	S	Runs well	27.1 tok/s	7152 ms	61K
Agentic Coding	S	Tight fit	27.1 tok/s	10403 ms	61K
Reasoning	S	Runs well	27.1 tok/s	8453 ms	61K
RAG	S	Tight fit	27.1 tok/s	13004 ms	61K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S87
Q3_K_S	3	14.7 GB	Low	S89
NVFP4	4	16.8 GB	Medium	S90
Q4_K_M	4	18.3 GB	Medium	S89
Q5_K_M	5	21.6 GB	High	S89
Q6_KBest for your GPU	6	24.6 GB	High	S89
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

More models your RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	58.6 tok/s
👁 Alibaba Qwen 3.5 35B A3B	35B	S	63.7 tok/s
👁 Alibaba Qwen 3 32B	32B	S	25.7 tok/s
👁 Alibaba Qwen 3 30B A3B	30.5B	S	69.7 tok/s

Frequently asked questions

See all results for RTX 5000 Ada 32GB See all hardware for Nemotron 3 Nano 30B

URL: https://willitrunai.com/can-run/nemotron-3-nano-30b-on-rtx-5000-ada-32gb

⇱ Nemotron 3 Nano 30B on RTX 5000 Ada 32GB? YES