Can Nemotron 3 Nano 30B run on RTX 5000 Ada Laptop 16GB?

Q: What should I upgrade first if Nemotron 3 Nano 30B feels slow on RTX 5000 Ada Laptop 16GB?

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 Q2_K

A79Great

Estimated from fit model

Nemotron 3 Nano 30B needs ~16.9 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q2_K quantization, expect ~22 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: 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 3 Nano 30B at Q4_K_M needs 23.5 GB — too much for RTX 5000 Ada Laptop 16GB (16.0 GB). Runs at Q2_K (16.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.5 GB, exceeds 16.0 GB available

23.5 GB required16.0 GB available

147% VRAM needed

7.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.2 tok/s

TTFT

23560 ms

Safe context

Memory

23.5 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron 3 Nano 30B on RTX 5000 Ada Laptop 16GB
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: 8.2 tok/s decode · 23.6s TTFT (warm) · 21 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 10% 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 0.7 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	F	Too heavy	8.6 tok/s	12350 ms	4K
Coding	F	Too heavy	7.6 tok/s	25327 ms	4K
Agentic Coding	F	Too heavy	6.2 tok/s	45344 ms	4K
Reasoning	F	Too heavy	7.6 tok/s	29932 ms	4K
RAG	F	Too heavy	6.2 tok/s	56680 ms	4K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	F0
Q3_K_S	3	14.7 GB	Low	F0
NVFP4	4

Get started

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

Run

ollama run nemotron-nano:30b

Upgrade options

Hardware that runs Nemotron 3 Nano 30B well

RTX 3090 24GBBudget pick

24 GB VRAM (+8)936 GB/s (+360)

Makes the model fit on the accelerator instead of staying completely out of reach.28 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.

~$1,499 MSRP

RTX 4090 24GBBest value

24 GB VRAM (+8)1008 GB/s (+432)

Makes the model fit on the accelerator instead of staying completely out of reach.32.8 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.

~$1,599 MSRP

RTX 5090 32GBNVIDIA upgrade

32 GB VRAM (+16)1792 GB/s (+1216)

Makes the model fit on the accelerator instead of staying completely out of reach.70.5 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.

~$1,999 MSRP

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run Nemotron 3 Nano 30B?

Yes, RTX 5000 Ada Laptop 16GB can run Nemotron 3 Nano 30B at Q2_K quantization (Runs with offload (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 23.5 GB which exceeds available memory, but at Q2_K it needs only 16.9 GB. Expected decode speed: 21.8 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 23.5 GB at Q4_K_M quantization. On RTX 5000 Ada Laptop 16GB, it fits at Q2_K using 16.9 GB.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization is Q4_K_M, but on RTX 5000 Ada Laptop 16GB the best fitting quantization is Q2_K, which uses 16.9 GB.

What speed will Nemotron 3 Nano 30B run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Nemotron 3 Nano 30B achieves approximately 21.8 tokens per second decode speed with a time-to-first-token of 8866ms using Q2_K quantization.

Can RTX 5000 Ada Laptop 16GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on RTX 5000 Ada Laptop 16GB receives a F grade with 7.6 tok/s and 4K context.

What context window can Nemotron 3 Nano 30B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, Nemotron 3 Nano 30B can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for Nemotron 3 Nano 30B

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