Can Nemotron Nano 9B v2 run on RTX 3080 10GB?

YES — With Offload

A83Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~9.8 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: 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) — 9.8 GB, 95.0 tok/s, Runs with offload

9.8 GB required10.0 GB available

98% VRAM used

Fit status

Runs with offload

Decode

95.0 tok/s

TTFT

2038 ms

Safe context

17K

Memory

9.8 GB / 10.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on RTX 3080 10GB
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: 95.0 tok/s decode · 2.0s TTFT (warm) · 238 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	95.0 tok/s	1112 ms	17K
Coding	A	Runs with offload	95.0 tok/s	2038 ms	17K
Agentic Coding	F	Too heavy	46.3 tok/s	6082 ms	17K
Reasoning	A	Runs with offload	95.0 tok/s	2408 ms	17K
RAG	F	Too heavy	46.3 tok/s	7603 ms	17K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A81
Q3_K_S	3	4.4 GB	Low	A82
NVFP4	4	5.0 GB	Medium	A82
Q4_K_M	4	5.5 GB	Medium	A82
Q5_K_MBest for your GPU	5	6.5 GB	High	A82
Q6_K	6	7.4 GB	High	F0
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

Frequently asked questions

Can RTX 3080 10GB run Nemotron Nano 9B v2?

Yes, RTX 3080 10GB can run Nemotron Nano 9B v2 with a A grade (Runs with offload). Expected decode speed: 95.0 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 9.8 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 3080 10GB?

On RTX 3080 10GB, Nemotron Nano 9B v2 achieves approximately 95.0 tokens per second decode speed with a time-to-first-token of 2038ms using Q4_K_M quantization.

Can RTX 3080 10GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on RTX 3080 10GB receives a A grade with 95.0 tok/s and 17K context.

What context window can Nemotron Nano 9B v2 use on RTX 3080 10GB?

On RTX 3080 10GB, Nemotron Nano 9B v2 can safely use up to 17K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron Nano 9B v2 feels slow on RTX 3080 10GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 3080 10GB See all hardware for Nemotron Nano 9B v2

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