Can Nemotron 3 Nano 30B run on NVIDIA A16 64GB?

YES — Runs Great

S88Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~28.3 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~28 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) — 28.3 GB, 27.5 tok/s, Runs well

28.3 GB required64.0 GB available

44% VRAM used

Fit status

Runs well

Decode

27.5 tok/s

TTFT

7042 ms

Safe context

131K

Memory

28.3 GB / 64.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B 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: 27.5 tok/s decode · 7.0s TTFT (warm) · 69 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	27.5 tok/s	3841 ms	131K
Coding	S	Runs well	27.5 tok/s	7042 ms	131K
Agentic Coding	S	Runs well	27.5 tok/s	10243 ms	131K
Reasoning	S	Runs well	27.5 tok/s	8322 ms	131K
RAG	S	Runs well	27.5 tok/s	12804 ms	131K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A82
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4	16.8 GB	Medium	A83
Q4_K_M	4	18.3 GB	Medium	A83
Q5_K_M	5	21.6 GB	High	A84
Q6_K	6	24.6 GB	High	A85
Q8_0Best for your GPU	8	32.1 GB	Very High	S87
F16	16	61.5 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Your hardware

More models your NVIDIA A16 64GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.8 tok/s
Qwen 3.6 35B A3B	35B	S	59.5 tok/s
Qwen 3.5 35B A3B	35B	S	64.7 tok/s
Qwen 3 32B	32B	S	26.1 tok/s
Qwen 3 30B A3B	30.5B	S	70.8 tok/s

Frequently asked questions

Can NVIDIA A16 64GB run Nemotron 3 Nano 30B?

Yes, NVIDIA A16 64GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 27.5 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 28.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron 3 Nano 30B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Nemotron 3 Nano 30B achieves approximately 27.5 tokens per second decode speed with a time-to-first-token of 7042ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on NVIDIA A16 64GB receives a S grade with 27.5 tok/s and 131K context.

What context window can Nemotron 3 Nano 30B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Nemotron 3 Nano 30B 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 3 Nano 30B

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<iframe src="https://willitrunai.com/embed/nemotron-3-nano-30b-on-a16-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

27.5 tok/s

3841 ms

131K

Coding

Runs well

27.5 tok/s

7042 ms

131K

Agentic Coding

Runs well

27.5 tok/s

10243 ms

131K

Reasoning

Runs well

27.5 tok/s

8322 ms

131K

RAG

Runs well

27.5 tok/s

12804 ms

131K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A82
Q3_K_S	3	14.7 GB	Low	A82
NVFP4	4	16.8 GB	Medium	A83
Q4_K_M	4	18.3 GB	Medium	A83
Q5_K_M	5	21.6 GB	High	A84
Q6_K	6	24.6 GB	High	A85
Q8_0Best for your GPU	8	32.1 GB	Very High	S87
F16	16	61.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

70.8 tok/s

Qwen 3.6 35B A3B

35B

59.5 tok/s

Qwen 3.5 35B A3B

35B

64.7 tok/s

Qwen 3 32B

32B

26.1 tok/s

Qwen 3 30B A3B

30.5B

70.8 tok/s

Frequently asked questions

Can NVIDIA A16 64GB run Nemotron 3 Nano 30B?

Yes, NVIDIA A16 64GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 27.5 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 28.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization for Nemotron 3 Nano 30B is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron 3 Nano 30B run at on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Nemotron 3 Nano 30B achieves approximately 27.5 tokens per second decode speed with a time-to-first-token of 7042ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on NVIDIA A16 64GB receives a S grade with 27.5 tok/s and 131K context.

What context window can Nemotron 3 Nano 30B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, Nemotron 3 Nano 30B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.