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NVIDIA: Nemotron 3.5 Lightning

nemotron-3-5-lightning
  • Open weights
  • 31.6B params
  • 262,144 context
  • Released 2026-08-01(HF repo created)
0/27axes covered

How to run it

Run it yourself

Parameters31.6B
ArchitectureNemotronHForCausalLM

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nvidia/NVIDIA-Nemotron-3.5-Lightning-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 →

Use it hosted

API model IDnvidia/nemotron-3.5-lightning
Context length262,144 tokens
Max output235,929 tokens
Input price$0.07 per 1M tokens
Cached input price$0.04 per 1M tokens
Output price$0.20 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasLogprobsMax tokensMin-pPresence penaltyReasoningRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-kTop logprobsTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"nvidia/nemotron-3.5-lightning","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

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.

No benchmark we track has published a result for this model yet, so there is no score card — only the run guide above. It will appear here once any board we ingest scores it.

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