Can Nemotron 3 Nano 30B run on Tesla P40 24GB?

YES — With Offload

S87Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~24.0 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 24.0 GB, 8.7 tok/s, Runs with offload (needs ~0 GB host RAM)

24.0 GB required24.0 GB available

100% VRAM used

Fit status

Runs with offload (needs ~0 GB host RAM)

Decode

8.7 tok/s

TTFT

22217 ms

Safe context

16K

Memory

24.0 GB / 24.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on Tesla P40 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: 8.7 tok/s decode · 22.2s TTFT (warm) · 22 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

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	12.0 tok/s	8806 ms	16K
Coding	S	Runs with offload (needs ~0 GB host RAM)	8.7 tok/s	22217 ms	16K
Agentic Coding	A	Very compromised (needs ~1.7 GB host RAM)	7.0 tok/s	39963 ms	16K
Reasoning	S	Runs with offload (needs ~0 GB host RAM)	8.7 tok/s	26257 ms	16K
RAG	A	Very compromised (needs ~1.7 GB host RAM)	7.0 tok/s	49954 ms	16K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on Tesla P40 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 Tesla P40 24GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	30.9 tok/s
Qwen 3.6 35B A3B	35B	A	12.7 tok/s
Qwen 3.5 35B A3B	35B	A	17.1 tok/s
Qwen 3 32B	32B	A	6.5 tok/s
Qwen 3 30B A3B	30.5B	S	30.9 tok/s

Frequently asked questions

Can Tesla P40 24GB run Nemotron 3 Nano 30B?

Yes, Tesla P40 24GB can run Nemotron 3 Nano 30B with a S grade (Runs with offload (needs ~0 GB host RAM)). Expected decode speed: 8.7 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 24.0 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 Tesla P40 24GB?

On Tesla P40 24GB, Nemotron 3 Nano 30B achieves approximately 8.7 tokens per second decode speed with a time-to-first-token of 22217ms using Q4_K_M quantization.

Can Tesla P40 24GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on Tesla P40 24GB receives a S grade with 8.7 tok/s and 16K context.

What context window can Nemotron 3 Nano 30B use on Tesla P40 24GB?

On Tesla P40 24GB, Nemotron 3 Nano 30B can safely use up to 16K 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 Tesla P40 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for Tesla P40 24GB See all hardware for Nemotron 3 Nano 30B

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