Models / NVIDIA/ Nemotron 3 Nano 30B A3B

Nemotron 3 Nano 30B A3B

NVIDIA · released Dec 4, 2025 · nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16

Input: text. Output: text.InputOutput
Type
Open weightsCustom licence
Params
31.6B
Context
262K

3B active per word · about 197K words of context · download allowed, licence restricts use

Our take

Written Aug 3, 2026

Nemotron 3 Nano is a compact text-only model from NVIDIA that uses a mixture-of-experts design with 3 billion active parameters out of 31.6 billion total. It is priced uniformly across all tracked providers and scores highest on coding tasks, though its creative writing lags well behind.

Who should pick it

Pick this for low-cost text generation workloads where you need long context up to 262,144 tokens, or where efficient inference with a small active-parameter footprint matters more than top benchmark scores. Use it for coding assistance, its strongest measured skill. Skip it if you need image, video or audio input, if you require a permissive open licence like Apache or MIT, or if creative writing quality is central to your use case.

The case for it

  • Only 3 billion active parameters out of 31.6 billion total — roughly one in ten parameters fires per token, making inference unusually efficient for the model's scale.
  • Same low price at every tracked provider, with no premium for faster endpoints.
  • Coding is its standout skill: its Arena Coding score sits about 47 points above its own general chat score.

The case against it

  • Creative writing is its weakest measured skill, trailing its coding score by more than 114 points.
  • Custom licence — not Apache 2.0 or MIT — so commercial terms and redistribution rights are unverified in our data.
  • Text-to-text only; no image, video or audio input accepted.
00

How good is it?

IntelligencePuzzles, maths, exam questions

1.5 of 5

Arena Text (overall)120th of 143 · 1315.3

Arena Hard Prompts 114th of 143Arena Maths 99th of 139

CodingWriting and fixing code on its own

1.5 of 5

Arena Coding112th of 143 · 1362.7

Arena Coding is the only board that has scored it for this.

AgenticPlanning, calling tools, staying on task

not measured

Nobody we watch has scored Nemotron 3 Nano 30B A3B for this. We would take the rating from Arena Agent (IPS).

WritingWe do not rate this

Scored, not ratedThe placings are on the right.

Two boards come close and neither tests writing: Arena Creative Writing asks people which of two replies they prefer, and LiveBench Language tests whether a model understood a passage. So we show where Nemotron 3 Nano 30B A3B placed and give it no mark out of five.

Arena Creative Writing 127th of 143 · 1248.3
Also scored, on boards we give no mark for
Arena Instruction Following 121st of 143

These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.

Every published score for this model6 scoresEvery figure we hold, from 6 boards, with who ran it and a link to the source — including the boards no rating above is built on.
1362.7independentsource ↗
1327.5independentsource ↗
1351.8independentsource ↗
1315.3independentsource ↗
01

Can you run it yourself?

Fits in memory
weights load entirely on the card
Spills to system RAM
some weights offload; much slower
Too large
will not load even with offload
est
size is calculated; the verdict could change by 10%
A card many people ownFits in memoryest

GeForce RTX 4090 · 24 GB

Weights at Q4_K_M19.9 / 24 GBest
Spare memory1 GB spare
Usable context16K of 262K
Decode speed377 tok/sest

Borderline fit on an estimated size. It leaves 1 GB spare on a size we calculated rather than measured, and a 10% error either way would change the answer.

Comfortable fit

One step upFits in memory

GeForce RTX 5090 · 32 GB

Weights at Q4_K_M19.9 / 32 GBest
Spare memory9 GB spare
Usable context131K of 262K
Decode speed670 tok/sest

Room to spare. 9 GB spare means a 10% error in the size would not change the answer.

On a MacFits in memory

Apple M1 Pro (16-core GPU) · 32 GB

Weights at Q4_K_M19.9 / 32 GBest
Spare memory2.2 GB spare
Usable context33K of 262K
Decode speed65 tok/sest

Room to spare. 2.2 GB spare means a 10% error in the size would not change the answer.

Your hardware
Checking your profile…

Memory use by level

Against a 24 GB card.

Q4_K_M
recommended
19.9 GBest
Fits in memoryest
Q5_K_M
23.4 GBest
Spills to system RAM
Q8_0
34.9 GBest
Too largeest

All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.

Check against your own machine → · All 71 devices, with every size →

02

Or rent it from someone else

Cheapest published offer

Cheapest of 6 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.

per 1M tokens
$0.050 in / $0.20 out
Context served
262K
Throughput
Not measured
Current provider offers with price, context and prompt-privacy answers
ProviderIn / out per 1M tokensContextThroughputTrains on promptsLogs promptsZero retention
OpenRouter$0.050 / $0.20262Knot measuredUnknownUnknownUnknown
DeepInfrafp4$0.050 / $0.20262Knot measuredUnknownUnknownUnknown
DeepInfrafp4$0.050 / $0.20262K163 tok/sNoNoConfirmed
Novita AI$0.050 / $0.20262Knot measuredUnknownUnknownUnknown
Novita AIfp4$0.050 / $0.20262K155 tok/sNoNoConfirmed
Crusoefp8$0.050 / $0.20262K113 tok/sNoNoConfirmed

Across the 6 listings we hold: 3 say they do not train on prompts, 0 say they do and 3 do not say. 3 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.

What each host's API supports

From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.

Supported
Not supported
Not published
host gave no parameter list
API features per host
ProviderTool callingJSON outputStrict schema
OpenRouter
DeepInfrafp4
DeepInfrafp4
Novita AI
Novita AIfp4
Crusoefp8

Tool calling: 4 of 6 listings say yes, 2 publish no parameter list. JSON output: 4 of 6 listings say yes, 2 publish no parameter list. Strict schema: 2 of 6 listings say yes, 2 say no, 2 publish no parameter list.

03

When we formed this view

Dates behind this page

Aug 2, 2026BenchmarkScored 1362.7 on Arena Codingleaderboard
Aug 2, 2026BenchmarkScored 1248.3 on Arena Creative Writingleaderboard
Aug 2, 2026BenchmarkScored 1327.5 on Arena Hard Promptsleaderboard
Aug 2, 2026BenchmarkScored 1292.2 on Arena Instruction Followingleaderboard
Aug 2, 2026BenchmarkScored 1351.8 on Arena Mathsleaderboard
Aug 2, 2026BenchmarkScored 1315.3 on Arena Text (overall)leaderboard
Jul 31, 2026Price changeOpenRouter raised Nemotron 3 Nano 30B A3B pricing by 20%cache read +20% ($0.025 → $0.030 per 1M tokens)
Jul 26, 2026ListedListed on LLMapfirst indexed by our pipeline
Dec 4, 2025AnnouncedNemotron 3 Nano 30B A3B announced by NVIDIA

Prices last checked 4d ago

What we do not know about this model yet

  • We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
  • 2 of 6 listings publish no parameter list, so what their API accepts is unknown to us.
  • Nothing we hold says whether an endpoint streams, so we do not show it either way.
  • 3 of 6 listings do not say whether they train on prompts.
04

Licence and identifiers

What the licence allowsCustom licence, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.

Licence

Custom licence

restricted_openCustom licence — review the terms

This model ships custom license terms that don't map to a known template. We haven't parsed them, so commercial use, redistribution and derivatives are unverified — review the original terms before shipping.

Identifiers

Architecture
Mixture of experts
Modality record
text->text
Catalogue slug
nvidia-nemotron-3-nano-30b-a3b

Machine-readable model card (omc.json) →

Something wrong on this page? Tell us