Will It Run AI

Can Nemotron 3 Nano 30B run on RTX 4070 Ti Super 16GB?

YES — With Q2_K

A80Great
Estimated from fit model

Nemotron 3 Nano 30B needs ~16.9 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q2_K quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
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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 4070 Ti Super 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

10.5 tok/s

TTFT

18427 ms

Safe context

4K

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 4070 Ti Super 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: 10.5 tok/s decode · 18.4s TTFT (warm) · 26 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy11.8 tok/s8986 ms4K
CodingFToo heavy10.5 tok/s18427 ms4K
Agentic CodingFToo heavy8.5 tok/s32991 ms4K
ReasoningFToo heavy10.5 tok/s21778 ms4K
RAGFToo heavy8.5 tok/s41239 ms4K

Quantization options

How Nemotron 3 Nano 30B (30B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.7 GB
LowF0
Q3_K_S
3
14.7 GB
LowF0
NVFP4
4
16.8 GB
MediumF0
Q4_K_M
4
18.3 GB
MediumF0
Q5_K_M
5
21.6 GB
HighF0
Q6_K
6
24.6 GB
HighF0
Q8_0
8
32.1 GB
Very HighF0
F16
16
61.5 GB
MaximumF0

Get started

Copy-paste commands to run Nemotron 3 Nano 30B on your machine.

Run

ollama run nemotron-nano:30b

Opciones de mejora

Hardware que ejecuta bien Nemotron 3 Nano 30B

NVIDIARTX 4000 Ada 20GBMejora NVIDIA
20 GB VRAM (+4)
A
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.8.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,250 MSRP

NVIDIARTX 3090 24GBOpción económica
24 GB VRAM (+8)936 GB/s (+264)
S
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.28 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,499 MSRP

NVIDIARTX 4090 24GBMejor relación calidad-precio
24 GB VRAM (+8)1008 GB/s (+336)
S
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.32.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

NVIDIARTX 5090 32GBMayor salto
32 GB VRAM (+16)1792 GB/s (+1120)
S
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.70.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,999 MSRP

Frequently asked questions

Can RTX 4070 Ti Super 16GB run Nemotron 3 Nano 30B?

Yes, RTX 4070 Ti Super 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: 27.9 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 4070 Ti Super 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 4070 Ti Super 16GB the best fitting quantization is Q2_K, which uses 16.9 GB.

What speed will Nemotron 3 Nano 30B run at on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, Nemotron 3 Nano 30B achieves approximately 27.9 tokens per second decode speed with a time-to-first-token of 6934ms using Q2_K quantization.

Can RTX 4070 Ti Super 16GB run Nemotron 3 Nano 30B for coding?

For coding workloads, Nemotron 3 Nano 30B on RTX 4070 Ti Super 16GB receives a F grade with 10.5 tok/s and 4K context.

What context window can Nemotron 3 Nano 30B use on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 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 4070 Ti Super 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 4070 Ti Super 16GBSee all hardware for Nemotron 3 Nano 30B
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