Can Nemotron 3 Nano 30B run on RTX 5000 Ada 32GB?

YES — Runs Great

S93Excellent

Estimated from fit model

Nemotron 3 Nano 30B needs ~25.1 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~27 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) — 25.1 GB, 27.1 tok/s, Runs well

25.1 GB required32.0 GB available

78% VRAM used

Fit status

Runs well

Decode

27.1 tok/s

TTFT

7152 ms

Safe context

61K

Memory

25.1 GB / 32.0 GB

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsNemotron 3 Nano 30B on RTX 5000 Ada 32GB
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: 27.1 tok/s decode · 7.2s TTFT (warm) · 68 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	S	Runs well	27.1 tok/s	3901 ms	61K
Coding	S	Runs well	27.1 tok/s	7152 ms	61K
Agentic Coding	S	Tight fit	27.1 tok/s	10403 ms	61K
Reasoning	S	Runs well	27.1 tok/s	8453 ms	61K
RAG	S	Tight fit	27.1 tok/s	13004 ms	61K

Inference speed

Nemotron 3 Nano 30B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Nemotron 3 Nano 30B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~71 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	70.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.1	Fits
RTX 4090 24GB	24 GB	Q4_K_M	32.8	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	32.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	30.3	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	28.0	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	27.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	24.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	14.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	12.9	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	11.9	Too big
RTX 4070 12GB	12 GB	Q4_K_M	4.2	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.6	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 3 Nano 30B (30B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S87
Q3_K_S	3	14.7 GB	Low	S89
NVFP4	4	16.8 GB	Medium	S90
Q4_K_M	4	18.3 GB	Medium	S89
Q5_K_M	5	21.6 GB	High	S89
Q6_KBest for your GPU	6	24.6 GB	High	S89
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 RTX 5000 Ada 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	69.7 tok/s
Qwen 3.6 35B A3B	35B	S	58.6 tok/s
Qwen 3.5 35B A3B	35B	S	63.7 tok/s
Qwen 3 32B	32B	S	25.7 tok/s
Qwen 3 30B A3B	30.5B	S	69.7 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Nemotron 3 Nano 30B?

Yes, RTX 5000 Ada 32GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 27.1 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 25.1 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Nemotron 3 Nano 30B achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7152ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on RTX 5000 Ada 32GB receives a S grade with 27.1 tok/s and 61K context.

What context window can Nemotron 3 Nano 30B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Nemotron 3 Nano 30B can safely use up to 61K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada 32GB See all hardware for Nemotron 3 Nano 30B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/nemotron-3-nano-30b-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

27.1 tok/s

3901 ms

61K

Coding

Runs well

27.1 tok/s

7152 ms

61K

Agentic Coding

Tight fit

27.1 tok/s

10403 ms

61K

Reasoning

Runs well

27.1 tok/s

8453 ms

61K

RAG

Tight fit

27.1 tok/s

13004 ms

61K

Nemotron 3 Nano 30B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

70.5

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

38.1

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

38.1

Fits

RTX 4090 24GB

24 GB

Q4_K_M

32.8

Offloads

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

32.7

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

30.3

Offloads

RTX 3090 24GB

24 GB

Q4_K_M

28.0

Offloads

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

27.3

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

25.8

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

24.0

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

14.1

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

12.9

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

11.9

Too big

RTX 4070 12GB

12 GB

Q4_K_M

4.2

Too big

RTX 3060 12GB

12 GB

Q4_K_M

2.6

Too big

RTX 4060 8GB

8 GB

Q4_K_M

2.0

Too big

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	S87
Q3_K_S	3	14.7 GB	Low	S89
NVFP4	4	16.8 GB	Medium	S90
Q4_K_M	4	18.3 GB	Medium	S89
Q5_K_M	5	21.6 GB	High	S89
Q6_KBest for your GPU	6	24.6 GB	High	S89
Q8_0	8	32.1 GB	Very High	F0
F16	16	61.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

69.7 tok/s

Qwen 3.6 35B A3B

35B

58.6 tok/s

Qwen 3.5 35B A3B

35B

63.7 tok/s

Qwen 3 32B

32B

25.7 tok/s

Qwen 3 30B A3B

30.5B

69.7 tok/s

Frequently asked questions

Can RTX 5000 Ada 32GB run Nemotron 3 Nano 30B?

Yes, RTX 5000 Ada 32GB can run Nemotron 3 Nano 30B with a S grade (Runs well). Expected decode speed: 27.1 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 25.1 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 RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Nemotron 3 Nano 30B achieves approximately 27.1 tokens per second decode speed with a time-to-first-token of 7152ms using Q4_K_M quantization.

Can RTX 5000 Ada 32GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on RTX 5000 Ada 32GB receives a S grade with 27.1 tok/s and 61K context.

What context window can Nemotron 3 Nano 30B use on RTX 5000 Ada 32GB?

On RTX 5000 Ada 32GB, Nemotron 3 Nano 30B can safely use up to 61K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.