NVIDIA-Nemotron-Labs-Teacher-STEM

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What NVIDIA-Nemotron-Labs-Teacher-STEM is

NVIDIA-Nemotron-Labs-Teacher-STEM Model Summary Model Overview Model Developer: NVIDIA Corporation Model Dates: December 2025 - May 2026 Data Freshness: The post-training data has a cutoff date of May 2026. The pre-training data has a cutoff date of September 2025. What is Nemotron? NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. Description NVIDIA-Nemotron-Labs-Teacher-STEM is a specialized general-reasoning model in the Nemotron 3 Ultra family, trained by NVIDIA. It is produced by taking the post-trained Nemotron 3 Ultra student and applying an additional round of reasoning-focused supervised fine-tuning and reinforcement learning, yielding a model with strong general reasoning capabilities across a wide range of subjects — including math, code, the natural sciences, humanities, sociology, and tool use within these domains (including code execution and search). Within the broader recipe, this is one of more than ten domain-specialized teacher models that supply training signal to Multi-Teacher On-Policy Distillation (MOPD), the stage used to produce the final Nemotron 3 Ultra. It is released as a standalone checkpoint because it is a strong reasoning model in its own right, matching or outperforming DeepSeek V4 Pro (High) on benchmarks such as GPQA, MMLU-Pro, LiveCodeBench v6, IMOAnswerBench, and Apex Shortlist. Like other models in the family, it responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template. The model employs a hybrid Latent Mixture-of-Experts (LatentMoE) architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. Like the Super model, the Ultra model incorporates Multi-Token Prediction (MTP) layers for faster text generation and improved quality, and it is trained using an NVFP4 pre-training recipe to maximize compute efficiency. The model has 55B active parameters and 550B parameters in total. The supported languages include: English, French, Sp

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

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Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys nvidia-nemotron-labs-teacher-stem for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nvidia-nemotron-labs-teacher-stem below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"nvidia-nemotron-labs-teacher-stem","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Commercial licence needed

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