Can Nemotron Nano 9B v2 run on RTX 4000 Ada Laptop 12GB?

YES — Tight Fit

A82Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~10.3 GB VRAM. RTX 4000 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 10.3 GB, 61.8 tok/s, Tight fit

10.3 GB required12.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

61.8 tok/s

TTFT

3135 ms

Safe context

27K

Memory

10.3 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on RTX 4000 Ada Laptop 12GB
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: 61.8 tok/s decode · 3.1s TTFT (warm) · 154 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	61.8 tok/s	1710 ms	27K
Coding	A	Tight fit	61.8 tok/s	3135 ms	27K
Agentic Coding	A	Runs with offload (needs ~0.3 GB host RAM)	40.6 tok/s	6934 ms	27K
Reasoning	A	Tight fit	61.8 tok/s	3705 ms	27K
RAG	A	Runs with offload (needs ~0.3 GB host RAM)	40.6 tok/s	8668 ms	27K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A79
Q3_K_S	3	4.4 GB	Low	A81
NVFP4	4	5.0 GB	Medium	A81
Q4_K_M	4	5.5 GB	Medium	A82
Q5_K_M	5	6.5 GB	High	A82
Q6_KBest for your GPU	6	7.4 GB	High	A81
Q8_0	8	9.6 GB	Very High	F0
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 Laptop 12GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	A	23.8 tok/s
Ministral 3 14B	14B	A	23.7 tok/s
Phi-4 14B	14B	A	21.5 tok/s
Qwen 2.5 14B	14B	A	22.1 tok/s

Frequently asked questions

Can RTX 4000 Ada Laptop 12GB run Nemotron Nano 9B v2?

Yes, RTX 4000 Ada Laptop 12GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 61.8 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 10.3 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 Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Nemotron Nano 9B v2 achieves approximately 61.8 tokens per second decode speed with a time-to-first-token of 3135ms using Q4_K_M quantization.

Can RTX 4000 Ada Laptop 12GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on RTX 4000 Ada Laptop 12GB receives a A grade with 61.8 tok/s and 27K context.

What context window can Nemotron Nano 9B v2 use on RTX 4000 Ada Laptop 12GB?

On RTX 4000 Ada Laptop 12GB, Nemotron Nano 9B v2 can safely use up to 27K 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 Laptop 12GB See all hardware for Nemotron Nano 9B v2

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