Can Nemotron Nano 9B v2 run on RTX 2080 Ti 11GB?

YES — Tight Fit

A83Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~10.2 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~78 tok/s.

Runtime: OllamaCapacity: TightBandwidth: MediumStack: 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) — 10.2 GB, 78.4 tok/s, Tight fit

10.2 GB required11.0 GB available

93% VRAM used

Fit status

Tight fit

Decode

78.4 tok/s

TTFT

2469 ms

Safe context

21K

Memory

10.2 GB / 11.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.1 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on RTX 2080 Ti 11GB
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: 78.4 tok/s decode · 2.5s TTFT (warm) · 196 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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

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	S	Runs well	78.4 tok/s	1347 ms	21K
Coding	A	Tight fit	78.4 tok/s	2469 ms	21K
Agentic Coding	A	Very compromised (needs ~0.7 GB host RAM)	41.9 tok/s	6720 ms	21K
Reasoning	A	Tight fit	78.4 tok/s	2918 ms	21K
RAG	A	Very compromised (needs ~0.7 GB host RAM)	41.9 tok/s	8399 ms	21K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A80
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_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

Frequently asked questions

Can RTX 2080 Ti 11GB run Nemotron Nano 9B v2?

Yes, RTX 2080 Ti 11GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 78.4 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 10.2 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 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Nemotron Nano 9B v2 achieves approximately 78.4 tokens per second decode speed with a time-to-first-token of 2469ms using Q4_K_M quantization.

Can RTX 2080 Ti 11GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on RTX 2080 Ti 11GB receives a A grade with 78.4 tok/s and 21K context.

What context window can Nemotron Nano 9B v2 use on RTX 2080 Ti 11GB?

On RTX 2080 Ti 11GB, Nemotron Nano 9B v2 can safely use up to 21K 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 2080 Ti 11GB?

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 2080 Ti 11GB See all hardware for Nemotron Nano 9B v2

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