Can Nemotron 3 Nano 30B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

A82Great

Estimated from fit model

Nemotron 3 Nano 30B needs ~35.0 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~10 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) — 35.0 GB, 9.6 tok/s, Runs well

35.0 GB required108.8 GB available

32% VRAM used

Fit status

Runs well

Decode

9.6 tok/s

TTFT

20120 ms

Safe context

131K

Memory

35.0 GB / 108.8 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on NVIDIA DGX Spark 128GB
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: 9.6 tok/s decode · 20.1s TTFT (warm) · 24 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	9.6 tok/s	10975 ms	131K
Coding	A	Runs well	9.6 tok/s	20120 ms	131K
Agentic Coding	A	Runs well	9.6 tok/s	29266 ms	131K
Reasoning	A	Runs well	9.6 tok/s	23779 ms	131K
RAG	A	Runs well	9.6 tok/s	36582 ms	131K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	A80
Q3_K_S	3	14.7 GB	Low	A80
NVFP4	4	16.8 GB	Medium	A81
Q4_K_M	4	18.3 GB	Medium	A81
Q5_K_M	5	21.6 GB	High	A81
Q6_K	6	24.6 GB	High	A82
Q8_0	8	32.1 GB	Very High	A83
F16Best for your GPU	16	61.5 GB	Maximum	S88

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 DGX Spark 128GB can run

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	2.4 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S	24.8 tok/s
Qwen 3.5 122B A10B	122B	S	6.6 tok/s
Qwen 3.6 35B A3B	35B	S	20.8 tok/s
Qwen 3.5 35B A3B	35B	S	22.6 tok/s

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Nemotron 3 Nano 30B?

Yes, NVIDIA DGX Spark 128GB can run Nemotron 3 Nano 30B with a A grade (Runs well). Expected decode speed: 9.6 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 35.0 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 DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Nemotron 3 Nano 30B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20120ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on NVIDIA DGX Spark 128GB receives a A grade with 9.6 tok/s and 131K context.

What context window can Nemotron 3 Nano 30B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, 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.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Nemotron 3 Nano 30B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for Nemotron 3 Nano 30B

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