Can Nemotron Nano 9B v2 run on RTX 3500 Ada Laptop 12GB?

YES — Tight Fit

A81Great

Estimated from fit model

Nemotron Nano 9B v2 needs ~10.3 GB VRAM. RTX 3500 Ada Laptop 12GB has 12.0 GB. With Q4_K_M quantization, expect ~48 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 10.3 GB, 48.0 tok/s, Tight fit

10.3 GB required12.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

48.0 tok/s

TTFT

4031 ms

Safe context

27K

Memory

10.3 GB / 12.0 GB

Memory breakdown

Weights5.5 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsNemotron Nano 9B v2 on RTX 3500 Ada Laptop 12GB
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: 48.0 tok/s decode · 4.0s TTFT (warm) · 120 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	A	Runs well	48.0 tok/s	2199 ms	27K
Coding	A	Tight fit	48.0 tok/s	4031 ms	27K
Agentic Coding	B	Runs with offload (needs ~0.3 GB host RAM)	31.6 tok/s	8915 ms	27K
Reasoning	A	Tight fit	48.0 tok/s	4764 ms	27K
RAG	B	Runs with offload (needs ~0.3 GB host RAM)	31.6 tok/s	11144 ms	27K

Inference speed

Nemotron Nano 9B v2 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron Nano 9B v2 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	126.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	119.6	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	109.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	90.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	86.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	79.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	79.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	74.0	Tight
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	47.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	46.5	Tight
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	43.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	40.9	Fits
RTX 4060 8GB	8 GB	Q4_K_M	18.5	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A79
Q3_K_S	3	4.4 GB	Low	A81
NVFP4	4	5.0 GB	Medium	A81
Q4_K_M	4	5.5 GB	Medium	A82
Q5_K_M	5	6.5 GB	High	A82
Q6_KBest for your GPU	6	7.4 GB	High	A81
Q8_0	8	9.6 GB	Very High	F0
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 RTX 3500 Ada Laptop 12GB can run

Model	Params	Grade	Decode
Qwen 3 14B	14B	A	18.5 tok/s
Ministral 3 14B	14B	A	18.4 tok/s
Phi-4 14B	14B	A	16.8 tok/s
Qwen 2.5 14B	14B	B	17.2 tok/s

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 9B v2?

Yes, RTX 3500 Ada Laptop 12GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 48.0 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 10.3 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 RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Nemotron Nano 9B v2 achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4031ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on RTX 3500 Ada Laptop 12GB receives a A grade with 48.0 tok/s and 27K context.

What context window can Nemotron Nano 9B v2 use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Nemotron Nano 9B v2 can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 3500 Ada Laptop 12GB See all hardware for Nemotron Nano 9B v2

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

48.0 tok/s

2199 ms

27K

Coding

Tight fit

48.0 tok/s

4031 ms

27K

Agentic Coding

Runs with offload (needs ~0.3 GB host RAM)

31.6 tok/s

8915 ms

27K

Reasoning

Tight fit

48.0 tok/s

4764 ms

27K

RAG

Runs with offload (needs ~0.3 GB host RAM)

31.6 tok/s

11144 ms

27K

Nemotron Nano 9B v2 inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

126.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

126.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

126.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

119.6

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

109.1

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

90.9

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

86.2

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

79.3

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

79.3

Fits

RTX 4070 12GB

12 GB

Q4_K_M

74.0

Tight

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

47.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

46.5

Tight

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

43.1

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

40.9

Fits

RTX 4060 8GB

8 GB

Q4_K_M

18.5

Too big

Quantization options

How Nemotron Nano 9B v2 (9B params) fits at each quantization level on RTX 3500 Ada Laptop 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	A79
Q3_K_S	3	4.4 GB	Low	A81
NVFP4	4	5.0 GB	Medium	A81
Q4_K_M	4	5.5 GB	Medium	A82
Q5_K_M	5	6.5 GB	High	A82
Q6_KBest for your GPU	6	7.4 GB	High	A81
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3 14B

14B

18.5 tok/s

Ministral 3 14B

14B

18.4 tok/s

Phi-4 14B

14B

16.8 tok/s

Qwen 2.5 14B

14B

17.2 tok/s

Frequently asked questions

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 9B v2?

Yes, RTX 3500 Ada Laptop 12GB can run Nemotron Nano 9B v2 with a A grade (Tight fit). Expected decode speed: 48.0 tok/s.

How much VRAM does Nemotron Nano 9B v2 need?

Nemotron Nano 9B v2 (9B parameters) requires approximately 10.3 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 RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Nemotron Nano 9B v2 achieves approximately 48.0 tokens per second decode speed with a time-to-first-token of 4031ms using Q4_K_M quantization.

Can RTX 3500 Ada Laptop 12GB run Nemotron Nano 9B v2 for coding?

For coding workloads, Nemotron Nano 9B v2 on RTX 3500 Ada Laptop 12GB receives a A grade with 48.0 tok/s and 27K context.

What context window can Nemotron Nano 9B v2 use on RTX 3500 Ada Laptop 12GB?

On RTX 3500 Ada Laptop 12GB, Nemotron Nano 9B v2 can safely use up to 27K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.