Can Nemotron Nano 8B run on RTX 4000 Ada 20GB?

YES — Runs Great

S86Excellent

○Estimated from fit model

Nemotron Nano 8B needs ~10.0 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 10.0 GB, 61.9 tok/s, Runs well

10.0 GB required20.0 GB available

50% VRAM used

Fit status

Runs well

Decode

61.9 tok/s

TTFT

3130 ms

Safe context

98K

Memory

10.0 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on RTX 4000 Ada 20GB
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: 61.9 tok/s decode · 3.1s TTFT (warm) · 155 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	61.9 tok/s	1707 ms	98K
Coding	S	Runs well	61.9 tok/s	3130 ms	98K
Agentic Coding	S	Runs well	61.9 tok/s	4552 ms	98K
Reasoning	S	Runs well	61.9 tok/s	3699 ms	98K
RAG	S	Runs well	61.9 tok/s	5691 ms	98K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A81
Q3_K_S	3	3.9 GB	Low	A81
NVFP4	4	4.5 GB	Medium	A82
Q4_K_M	4	4.9 GB	Medium	A82
Q5_K_M	5	5.8 GB	High	A82
Q6_K	6	6.6 GB	High	A83
Q8_0Best for your GPU	8	8.6 GB	Very High	A85
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron Nano 8B on your machine.

Run

lms load Llama-3.1-Nemotron-Nano-8B-v1 && lms server start

Your hardware

More models your RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	A	23.2 tok/s
👁 Alibaba Qwen 3.5 27B	27B	A	10.4 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	13 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	A	24.6 tok/s
👁 Alibaba Qwen 3.5 9B	9B	S	55 tok/s

Frequently asked questions

See all results for RTX 4000 Ada 20GB See all hardware for Nemotron Nano 8B

URL: https://willitrunai.com/can-run/nemotron-nano-8b-on-rtx-4000-ada-20gb

⇱ Nemotron Nano 8B on RTX 4000 Ada 20GB? YES