Can Nemotron Nano 8B run on RTX 3080 10GB?

YES — Tight Fit

S89Excellent

Estimated from fit model

Nemotron Nano 8B needs ~9.0 GB VRAM. RTX 3080 10GB has 10.0 GB. With Q4_K_M quantization, expect ~112 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) — 9.0 GB, 112.0 tok/s, Tight fit

9.0 GB required10.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

24K

Memory

9.0 GB / 10.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.0 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B 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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	112.0 tok/s	943 ms	24K
Coding	S	Tight fit	112.0 tok/s	1729 ms	24K
Agentic Coding	A	Very compromised (needs ~0.4 GB host RAM)	78.3 tok/s	3597 ms	24K
Reasoning	S	Tight fit	112.0 tok/s	2043 ms	24K
RAG	A	Very compromised (needs ~0.4 GB host RAM)	78.3 tok/s	4496 ms	24K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 3080 10GB (10.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	S86
Q3_K_S	3	3.9 GB	Low	S88
NVFP4	4	4.5 GB	Medium	S88
Q4_K_M	4	4.9 GB	Medium	S88
Q5_K_M	5	5.8 GB	High	S88
Q6_KBest for your GPU	6	6.6 GB	High	S87
Q8_0	8	8.6 GB	Very High	F0
F16	16	16.4 GB	Maximum	F0

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

Your hardware

More models your RTX 3080 10GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	113.1 tok/s

Frequently asked questions

Can RTX 3080 10GB run Nemotron Nano 8B?

Yes, RTX 3080 10GB can run Nemotron Nano 8B with a S grade (Tight fit). Expected decode speed: 112.0 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 9.0 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 3080 10GB?

On RTX 3080 10GB, Nemotron Nano 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 3080 10GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on RTX 3080 10GB receives a S grade with 112.0 tok/s and 24K context.

What context window can Nemotron Nano 8B use on RTX 3080 10GB?

On RTX 3080 10GB, Nemotron Nano 8B can safely use up to 24K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 3080 10GB See all hardware for Nemotron Nano 8B

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