Can Nemotron Nano 9B v2 run on RTX 4000 Ada 20GB?

YES — Runs Great

A82Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~11.1 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~55 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) — 11.1 GB, 55.0 tok/s, Runs well

11.1 GB required20.0 GB available

55% VRAM used

Fit status

Runs well

Decode

55.0 tok/s

TTFT

3521 ms

Safe context

74K

Memory

11.1 GB / 20.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 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: 55.0 tok/s decode · 3.5s TTFT (warm) · 138 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	55.0 tok/s	1921 ms	74K
Coding	A	Runs well	55.0 tok/s	3521 ms	74K
Agentic Coding	A	Runs well	55.0 tok/s	5122 ms	74K
Reasoning	A	Runs well	55.0 tok/s	4161 ms	74K
RAG	A	Runs well	55.0 tok/s	6402 ms	74K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A75
Q3_K_S	3	4.4 GB	Low	A76
NVFP4	4	5.0 GB	Medium	A76
Q4_K_M	4	5.5 GB	Medium	A77
Q5_K_M	5	6.5 GB	High	A77
Q6_K	6	7.4 GB	High	A78
Q8_0Best for your GPU	8	9.6 GB	Very High	A80
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 RTX 4000 Ada 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	23.2 tok/s
Qwen 3.5 27B	27B	A	10.4 tok/s
Qwen 3.6 27B	27B	S	13 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	24.6 tok/s
Magistral Small 2507	24B	S	15 tok/s

Frequently asked questions

Can RTX 4000 Ada 20GB run Nemotron Nano 9B v2?

Yes, RTX 4000 Ada 20GB can run Nemotron Nano 9B v2 with a A grade (Runs well). Expected decode speed: 55.0 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Nemotron Nano 9B v2 achieves approximately 55.0 tokens per second decode speed with a time-to-first-token of 3521ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on RTX 4000 Ada 20GB receives a A grade with 55.0 tok/s and 74K context.

What context window can Nemotron Nano 9B v2 use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Nemotron Nano 9B v2 can safely use up to 74K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Nemotron Nano 9B v2

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