Can Nemotron Nano 9B v2 run on Tesla P100 16GB?

YES — Runs Great

S85Excellent

Estimated from fit model

Nemotron Nano 9B v2 needs ~10.7 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~85 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) — 10.7 GB, 84.6 tok/s, Runs well

10.7 GB required16.0 GB available

67% VRAM used

Fit status

Runs well

Decode

84.6 tok/s

TTFT

2289 ms

Safe context

51K

Memory

10.7 GB / 16.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on Tesla P100 16GB
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: 84.6 tok/s decode · 2.3s TTFT (warm) · 211 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	84.6 tok/s	1249 ms	51K
Coding	S	Runs well	84.6 tok/s	2289 ms	51K
Agentic Coding	A	Tight fit	84.6 tok/s	3330 ms	51K
Reasoning	S	Runs well	84.6 tok/s	2706 ms	51K
RAG	A	Tight fit	84.6 tok/s	4162 ms	51K

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A77
Q3_K_S	3	4.4 GB	Low	A78
NVFP4	4	5.0 GB	Medium	A78
Q4_K_M	4	5.5 GB	Medium	A79
Q5_K_M	5	6.5 GB	High	A80
Q6_K	6	7.4 GB	High	A81
Q8_0Best for your GPU	8	9.6 GB	Very High	A81
F16	16	18.5 GB	Maximum	F0

Get started

Copy-paste commands to run Nemotron Nano 9B v2 on your machine.

Run

ollama run nemotron-nano:9b-v2

Your hardware

More models your Tesla P100 16GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	S	54.6 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	51.8 tok/s
GPT-OSS 20B	21B	A	46.4 tok/s
Ministral 3 14B	14B	S	54.4 tok/s
Codestral 2 25.08	22B	A	18 tok/s

Frequently asked questions

Can Tesla P100 16GB run Nemotron Nano 9B v2?

Yes, Tesla P100 16GB can run Nemotron Nano 9B v2 with a S grade (Runs well). Expected decode speed: 84.6 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 10.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Nemotron Nano 9B v2?

The recommended quantization for Nemotron Nano 9B v2 is Q4_K_M, which balances quality and memory efficiency.

What speed will Nemotron Nano 9B v2 run at on Tesla P100 16GB?

On Tesla P100 16GB, Nemotron Nano 9B v2 achieves approximately 84.6 tokens per second decode speed with a time-to-first-token of 2289ms using Q4_K_M quantization.

Can Tesla P100 16GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on Tesla P100 16GB receives a S grade with 84.6 tok/s and 51K context.

What context window can Nemotron Nano 9B v2 use on Tesla P100 16GB?

On Tesla P100 16GB, Nemotron Nano 9B v2 can safely use up to 51K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Tesla P100 16GB See all hardware for Nemotron Nano 9B v2

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