Can Nemotron 3 Nano 30B run on NVIDIA V100 32GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~25.1 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 25.1 GB, 35.4 tok/s, Runs well

25.1 GB required32.0 GB available

78% VRAM used

Fit status

Runs well

Decode

35.4 tok/s

TTFT

5466 ms

Safe context

61K

Memory

25.1 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on NVIDIA V100 32GB
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: 35.4 tok/s decode · 5.5s TTFT (warm) · 89 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	35.4 tok/s	2981 ms	61K
Coding	S	Runs well	35.4 tok/s	5466 ms	61K
Agentic Coding	S	Tight fit	35.4 tok/s	7950 ms	61K
Reasoning	S	Runs well	35.4 tok/s	6459 ms	61K
RAG	S	Tight fit	35.4 tok/s	9937 ms	61K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S87
Q3_K_S	3	14.7 GB	Low	S89
NVFP4	4

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 V100 32GB can run

Model	Params	Grade	Decode	Capabilities
Qwen3-Coder 30B A3B Instruct	30.5B	S	91.2 tok/s
Qwen 3.6 35B A3B	35B	S	76.6 tok/s

Frequently asked questions

Can NVIDIA V100 32GB run Nemotron 3 Nano 30B?

Yes, NVIDIA V100 32GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 35.4 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 25.1 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 V100 32GB?

On NVIDIA V100 32GB, Nemotron 3 Nano 30B achieves approximately 35.4 tokens per second decode speed with a time-to-first-token of 5466ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on NVIDIA V100 32GB receives a S grade with 35.4 tok/s and 61K context.

What context window can Nemotron 3 Nano 30B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Nemotron 3 Nano 30B can safely use up to 61K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Nemotron 3 Nano 30B

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