Can Nemotron Nano 8B run on NVIDIA A16 64GB?

YES — Runs Great

A82Great

Estimated from fit model

Nemotron Nano 8B needs ~14.4 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~103 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) — 14.4 GB, 103.1 tok/s, Runs well

14.4 GB required64.0 GB available

23% VRAM used

Fit status

Runs well

Decode

103.1 tok/s

TTFT

1878 ms

Safe context

131K

Memory

14.4 GB / 64.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsNemotron Nano 8B on NVIDIA A16 64GB
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: 103.1 tok/s decode · 1.9s TTFT (warm) · 258 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	A	Runs well	103.1 tok/s	1024 ms	131K
Coding	A	Runs well	103.1 tok/s	1878 ms	131K
Agentic Coding	A	Runs well	103.1 tok/s	2731 ms	131K
Reasoning	A	Runs well	103.1 tok/s	2219 ms	131K
RAG	A	Runs well	103.1 tok/s	3414 ms	131K

Quantization options

How Nemotron Nano 8B (8B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	A75
Q3_K_S	3	3.9 GB	Low	A75
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 NVIDIA A16 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.5 27B	27B	S	30.7 tok/s

Frequently asked questions

Can NVIDIA A16 64GB run Nemotron Nano 8B?

Yes, NVIDIA A16 64GB can run Nemotron Nano 8B with a A grade (Runs well). Expected decode speed: 103.1 tok/s.

How much VRAM does Nemotron Nano 8B need?

Nemotron Nano 8B (8B parameters) requires approximately 14.4 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 NVIDIA A16 64GB?

On NVIDIA A16 64GB, Nemotron Nano 8B achieves approximately 103.1 tokens per second decode speed with a time-to-first-token of 1878ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Nemotron Nano 8B for coding?

For coding workloads, Nemotron Nano 8B on NVIDIA A16 64GB receives a A grade with 103.1 tok/s and 131K context.

What context window can Nemotron Nano 8B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Nemotron Nano 8B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA A16 64GB See all hardware for Nemotron Nano 8B

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