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

Q: What should I upgrade first if Nemotron Nano 8B 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.

BARELY — Tight on Memory

A75Great

Estimated from fit model

Nemotron Nano 8B needs ~8.8 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 8.8 GB, 28.2 tok/s, Very compromised (needs ~0.5 GB host RAM)

8.8 GB required8.0 GB available

110% VRAM needed

0.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.5 GB host RAM)

Decode

28.2 tok/s

TTFT

6866 ms

Safe context

Memory

8.8 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B 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: 28.2 tok/s decode · 6.9s TTFT (warm) · 71 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 0.5 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	S	Runs with offload	46.3 tok/s	2280 ms	9K
Coding	A	Very compromised (needs ~0.5 GB host RAM)	28.2 tok/s	6866 ms	9K
Agentic Coding	F	Too heavy	18.5 tok/s	15207 ms	9K
Reasoning	A	Very compromised (needs ~0.5 GB host RAM)	28.2 tok/s	8114 ms	9K
RAG	F	Too heavy	18.5 tok/s	19009 ms

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S89
Q3_K_S	3	3.9 GB	Low	S88
NVFP4	4

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

Frequently asked questions

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

Yes, RTX 3000 Ada Laptop 8GB can run Nemotron Nano 8B with a A grade (Very compromised (needs ~0.5 GB host RAM)). Expected decode speed: 28.2 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 8B?

The recommended quantization for Nemotron Nano 8B is Q4_K_M, which balances quality and memory efficiency.

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

On RTX 3000 Ada Laptop 8GB, Nemotron Nano 8B achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6866ms using Q4_K_M quantization.

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

For coding workloads, Nemotron Nano 8B on RTX 3000 Ada Laptop 8GB receives a A grade with 28.2 tok/s and 9K context.

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

On RTX 3000 Ada Laptop 8GB, Nemotron Nano 8B can safely use up to 9K tokens of context. 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 8B

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