Can Nemotron 3 Nano 30B run on RTX A4000 16GB?

YES — With Q2_K

A78Great

Estimated from fit model

Nemotron 3 Nano 30B needs ~16.9 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q2_K quantization, expect ~16 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.

Nemotron 3 Nano 30B at Q4_K_M needs 23.5 GB — too much for RTX A4000 16GB (16.0 GB). Runs at Q2_K (16.9 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.5 GB, exceeds 16.0 GB available

23.5 GB required16.0 GB available

147% VRAM needed

7.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

6.1 tok/s

TTFT

31590 ms

Safe context

Memory

23.5 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights18.3 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsNemotron 3 Nano 30B on RTX A4000 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: 6.1 tok/s decode · 31.6s TTFT (warm) · 15 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	6.9 tok/s	15404 ms	4K
Coding	F	Too heavy	6.1 tok/s	31590 ms	4K
Agentic Coding	F	Too heavy	5.0 tok/s	56556 ms	4K
Reasoning	F	Too heavy	6.1 tok/s	37333 ms	4K
RAG	F	Too heavy	5.0 tok/s	70695 ms	4K

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 A4000 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	11.7 GB	Low	F0
Q3_K_S	3	14.7 GB	Low	F0
NVFP4	4	16.8 GB	Medium	F0
Q4_K_M	4	18.3 GB	Medium	F0
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

升级选项

能流畅运行 Nemotron 3 Nano 30B 的硬件

RTX 4000 Ada 20GBNVIDIA 升级

20 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.8.5 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 39%.

~$1,250 MSRP

RTX 3090 24GB经济之选

24 GB VRAM (+8)936 GB/s (+488)

Makes the model fit on the accelerator instead of staying completely out of reach.28 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

RTX 4090 24GB最佳性价比

24 GB VRAM (+8)1008 GB/s (+560)

Makes the model fit on the accelerator instead of staying completely out of reach.32.8 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,599 MSRP

RTX 5090 32GB最大飞跃

32 GB VRAM (+16)1792 GB/s (+1344)

Makes the model fit on the accelerator instead of staying completely out of reach.70.5 tok/s 解码

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,999 MSRP

Frequently asked questions

Can RTX A4000 16GB run Nemotron 3 Nano 30B?

Yes, RTX A4000 16GB can run Nemotron 3 Nano 30B at Q2_K quantization (Runs with offload (needs ~0.7 GB host RAM)). The recommended Q4_K_M requires 23.5 GB which exceeds available memory, but at Q2_K it needs only 16.9 GB. Expected decode speed: 16.3 tok/s.

How much VRAM does Nemotron 3 Nano 30B need?

Nemotron 3 Nano 30B (30B parameters) requires approximately 23.5 GB at Q4_K_M quantization. On RTX A4000 16GB, it fits at Q2_K using 16.9 GB.

What is the best quantization for Nemotron 3 Nano 30B?

The recommended quantization is Q4_K_M, but on RTX A4000 16GB the best fitting quantization is Q2_K, which uses 16.9 GB.

What speed will Nemotron 3 Nano 30B run at on RTX A4000 16GB?

On RTX A4000 16GB, Nemotron 3 Nano 30B achieves approximately 16.3 tokens per second decode speed with a time-to-first-token of 11888ms using Q2_K quantization.

Can RTX A4000 16GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on RTX A4000 16GB receives a F grade with 6.1 tok/s and 4K context.

What context window can Nemotron 3 Nano 30B use on RTX A4000 16GB?

On RTX A4000 16GB, Nemotron 3 Nano 30B can safely use up to 10K tokens of context at Q2_K quantization. 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 RTX A4000 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX A4000 16GB See all hardware for Nemotron 3 Nano 30B

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