Can Nemotron Nano 9B v2 run on RTX 3000 Ada Laptop 8GB?

Q: What should I upgrade first if Nemotron Nano 9B v2 feels slow on RTX 3000 Ada Laptop 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With NVFP4

B63Good

Estimated from fit model

Nemotron Nano 9B v2 needs ~9.5 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With NVFP4 quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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.

Nemotron Nano 9B v2 at Q4_K_M needs 9.9 GB — too much for RTX 3000 Ada Laptop 8GB (8.0 GB). Runs at NVFP4 (9.5 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.9 GB, exceeds 8.0 GB available

9.9 GB required8.0 GB available

124% VRAM needed

1.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

19.6 tok/s

TTFT

9885 ms

Safe context

Memory

9.9 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron Nano 9B v2 on RTX 3000 Ada Laptop 8GB
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: 19.6 tok/s decode · 9.9s TTFT (warm) · 49 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Very compromised (needs ~0.4 GB host RAM)	25.8 tok/s	4091 ms	4K
Coding	F	Too heavy	19.6 tok/s	9885 ms	4K
Agentic Coding	F	Too heavy	12.3 tok/s	22838 ms	4K
Reasoning	F	Too heavy	19.6 tok/s	11683 ms	4K
RAG	F	Too heavy	12.3 tok/s	28548 ms	4K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A83
Q3_K_S	3	4.4 GB	Low	A83
NVFP4Best for your GPU

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Upgrade options

Hardware that runs Nemotron Nano 9B v2 well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+72)

Makes the model fit on the accelerator instead of staying completely out of reach.46.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)448 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.54.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.41.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$499 MSRP

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run Nemotron Nano 9B v2?

Yes, RTX 3000 Ada Laptop 8GB can run Nemotron Nano 9B v2 at NVFP4 quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 9.9 GB which exceeds available memory, but at NVFP4 it needs only 9.5 GB. Expected decode speed: 24.7 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 9.9 GB at Q4_K_M quantization. On RTX 3000 Ada Laptop 8GB, it fits at NVFP4 using 9.5 GB.

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

The recommended quantization is Q4_K_M, but on RTX 3000 Ada Laptop 8GB the best fitting quantization is NVFP4, which uses 9.5 GB.

What speed will Nemotron Nano 9B v2 run at on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Nemotron Nano 9B v2 achieves approximately 24.7 tokens per second decode speed with a time-to-first-token of 7839ms using NVFP4 quantization.

Can RTX 3000 Ada Laptop 8GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on RTX 3000 Ada Laptop 8GB receives a F grade with 19.6 tok/s and 4K context.

What context window can Nemotron Nano 9B v2 use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, Nemotron Nano 9B v2 can safely use up to 6K tokens of context at NVFP4 quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 3000 Ada Laptop 8GB See all hardware for Nemotron Nano 9B v2

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