Can Nemotron Nano 8B run on RTX 4080 Laptop 12GB?

YES — Runs Great

S91Excellent

Estimated from fit model

Nemotron Nano 8B needs ~9.2 GB VRAM. RTX 4080 Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~74 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.2 GB, 74.2 tok/s, Runs well

9.2 GB required12.0 GB available

77% VRAM used

Fit status

Runs well

Decode

74.2 tok/s

TTFT

2608 ms

Safe context

39K

Memory

9.2 GB / 12.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on RTX 4080 Laptop 12GB
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: 74.2 tok/s decode · 2.6s TTFT (warm) · 186 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	74.2 tok/s	1423 ms	39K
Coding	S	Runs well	74.2 tok/s	2608 ms	39K
Agentic Coding	S	Tight fit	74.2 tok/s	3794 ms	39K
Reasoning	S	Runs well	74.2 tok/s	3082 ms	39K
RAG	S	Tight fit	74.2 tok/s	4742 ms	39K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 4080 Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A85
Q3_K_S	3	3.9 GB	Low	S86
NVFP4	4	4.5 GB	Medium	S86
Q4_K_M	4	4.9 GB	Medium	S87
Q5_K_M	5	5.8 GB	High	S87
Q6_K	6	6.6 GB	High	S87
Q8_0Best for your GPU	8	8.6 GB	Very High	S87
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 4080 Laptop 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	66 tok/s
Qwen 3 14B	14B	A	25.4 tok/s

Frequently asked questions

Can RTX 4080 Laptop 12GB run Nemotron Nano 8B?

Yes, RTX 4080 Laptop 12GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 74.2 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 9.2 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 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, Nemotron Nano 8B achieves approximately 74.2 tokens per second decode speed with a time-to-first-token of 2608ms using Q4_K_M quantization.

Can RTX 4080 Laptop 12GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on RTX 4080 Laptop 12GB receives a S grade with 74.2 tok/s and 39K context.

What context window can Nemotron Nano 8B use on RTX 4080 Laptop 12GB?

On RTX 4080 Laptop 12GB, Nemotron Nano 8B can safely use up to 39K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 4080 Laptop 12GB See all hardware for Nemotron Nano 8B

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