Can Nemotron Nano 8B run on RTX 3500 Ada Laptop 12GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Nemotron Nano 8B needs ~9.2 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~54 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, 54.0 tok/s, Runs well

9.2 GB required12.0 GB available

77% VRAM used

Fit status

Runs well

Decode

54.0 tok/s

TTFT

3583 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 3500 Ada 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: 54.0 tok/s decode · 3.6s TTFT (warm) · 135 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	54.0 tok/s	1954 ms	39K
Coding	S	Runs well	54.0 tok/s	3583 ms	39K
Agentic Coding	S	Tight fit	54.0 tok/s	5212 ms	39K
Reasoning	S	Runs well	54.0 tok/s	4235 ms	39K
RAG	S	Tight fit	54.0 tok/s	6515 ms	39K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 3500 Ada 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 3500 Ada Laptop 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	S	48 tok/s
Qwen 3 14B	14B	A	18.5 tok/s

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 8B?

Yes, RTX 3500 Ada Laptop 12GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 54.0 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Nemotron Nano 8B achieves approximately 54.0 tokens per second decode speed with a time-to-first-token of 3583ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on RTX 3500 Ada Laptop 12GB receives a S grade with 54.0 tok/s and 39K context.

What context window can Nemotron Nano 8B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada 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 3500 Ada Laptop 12GB See all hardware for Nemotron Nano 8B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

54.0 tok/s

1954 ms

39K

Coding

Runs well

54.0 tok/s

3583 ms

39K

Agentic Coding

Tight fit

54.0 tok/s

5212 ms

39K

Reasoning

Runs well

54.0 tok/s

4235 ms

39K

RAG

Tight fit

54.0 tok/s

6515 ms

39K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX 3500 Ada 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

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

48 tok/s

Qwen 3 14B

14B

18.5 tok/s

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 8B?

Yes, RTX 3500 Ada Laptop 12GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 54.0 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 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Nemotron Nano 8B achieves approximately 54.0 tokens per second decode speed with a time-to-first-token of 3583ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on RTX 3500 Ada Laptop 12GB receives a S grade with 54.0 tok/s and 39K context.

What context window can Nemotron Nano 8B use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada 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.