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Nvidia Nemotron 3 Nano 30b A3b Bf16

nvidia-nemotron-3-nano-30b-a3b-bf16
  • Open weights
  • NVIDIA Open Model licence
  • 31.6B params
  • Released 2025-12-04(HF repo created)
5/27axes covered
Overall · LMArena
1,314#251 of 396
Best percentile
39.6Coding
Cohort standing
38.2median 53.9

What the data says

Benchmarked on 5 of 27 tracked axes. Every figure below is the model's own published result on that board.

Weakest axes

  • 5.2 pct — Factuality Arena (1,362)

Uneven: 34.4 percentile points between its best and worst axis — 39.6 pct on Coding versus 5.2 pct on Factuality Arena.

  • Coding39.6 pct1,361Verify →
  • Hard prompts38.4 pct1,325Verify →
  • Overall36.9 pct1,314Verify →
  • Instruction following35.6 pct1,291Verify →
  • Factuality Arena5.2 pct1,362Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • LiveBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorynvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Parameters31.6B
ArchitectureNemotronHForCausalLM
LicenceNVIDIA Open Model

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • nvidia-nemotron-3-nano-30b-a3b-bf16 stands at 38.2 (raw mean 31.1) — measured on 5 of 27 axes.

    standingtemplateVerify →

  • nvidia-nemotron-3-nano-30b-a3b-bf16 stands at 38.2 versus a cohort median of 53.9 across 121 models released within 180 days — below the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →