Can Nemotron Nano 8B run on RTX A4500 20GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Nemotron Nano 8B needs ~10.0 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q4_K_M quantization, expect ~102 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 10.0 GB, 110.0 tok/s, Runs well

10.0 GB required20.0 GB available

50% VRAM used

Fit status

Runs well

Decode

110.0 tok/s

TTFT

1761 ms

Safe context

98K

Memory

10.0 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on RTX A4500 20GB
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: 110.0 tok/s decode · 1.8s TTFT (warm) · 275 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	110.0 tok/s	960 ms	98K
Coding	S	Runs well	102.3 tok/s	1893 ms	98K
Agentic Coding	S	Runs well	110.0 tok/s	2561 ms	98K
Reasoning	S	Runs well	110.0 tok/s	2081 ms	98K
RAG	S	Runs well	110.0 tok/s	3201 ms	98K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A81
Q3_K_S	3	3.9 GB	Low	A81
NVFP4	4

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 A4500 20GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	A	41.2 tok/s
Qwen 3.5 27B	27B	A	18.6 tok/s

Frequently asked questions

Can RTX A4500 20GB run Nemotron Nano 8B?

Yes, RTX A4500 20GB can run Nemotron Nano 8B with a S grade (Runs well). Expected decode speed: 102.3 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 10.0 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 A4500 20GB?

On RTX A4500 20GB, Nemotron Nano 8B achieves approximately 102.3 tokens per second decode speed with a time-to-first-token of 1893ms using Q4_K_M quantization.

Can RTX A4500 20GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on RTX A4500 20GB receives a S grade with 102.3 tok/s and 98K context.

What context window can Nemotron Nano 8B use on RTX A4500 20GB?

On RTX A4500 20GB, Nemotron Nano 8B can safely use up to 98K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX A4500 20GB See all hardware for Nemotron Nano 8B

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