Can Nemotron 3 Nano 30B run on NVIDIA L4 24GB?

YES — With Offload

S86Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~24.3 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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) — 24.3 GB, 8.3 tok/s, Runs with offload (needs ~0.3 GB host RAM)

24.3 GB required24.0 GB available

101% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

8.3 tok/s

TTFT

23215 ms

Safe context

14K

Memory

24.3 GB / 24.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on NVIDIA L4 24GB
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: 8.3 tok/s decode · 23.2s TTFT (warm) · 21 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 7.8 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	10.7 tok/s	9910 ms	14K
Coding	S	Runs with offload	7.8 tok/s	24956 ms	14K
Agentic Coding	A	Very compromised	6.3 tok/s	44390 ms	14K
Reasoning	S	Runs with offload	7.8 tok/s	29493 ms	14K
RAG	A	Very compromised	6.3 tok/s	55487 ms	14K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S90
Q3_K_S	3	14.7 GB	Low	S90
NVFP4	4	16.8 GB	Medium	S90
Q4_K_MBest for your GPU	4	18.3 GB	Medium	S89
Q5_K_M	5	21.6 GB	High	F0
Q6_K	6	24.6 GB	High	F0
Q8_0	8	32.1 GB	Very High	F0
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 L4 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	29.5 tok/s
Qwen 3.5 35B A3B	35B	A	17.7 tok/s
Qwen 3 32B	32B	A	6.3 tok/s
Qwen 3 30B A3B	30.5B	S	29.5 tok/s

Frequently asked questions

Can NVIDIA L4 24GB run Nemotron 3 Nano 30B?

Yes, NVIDIA L4 24GB can run Nemotron 3 Nano 30B with a S grade (Runs with offload). Expected decode speed: 7.8 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 24.3 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 L4 24GB?

On NVIDIA L4 24GB, Nemotron 3 Nano 30B achieves approximately 7.8 tokens per second decode speed with a time-to-first-token of 24956ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on NVIDIA L4 24GB receives a S grade with 7.8 tok/s and 14K context.

What context window can Nemotron 3 Nano 30B use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Nemotron 3 Nano 30B can safely use up to 14K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Nemotron 3 Nano 30B feels slow on NVIDIA L4 24GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

See all results for NVIDIA L4 24GB See all hardware for Nemotron 3 Nano 30B

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