NVIDIA-Nemotron-Labs-3-Puzzle

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What NVIDIA-Nemotron-Labs-3-Puzzle is

NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 Description: Nemotron-Labs-3-Puzzle-75B-A9B is a deployment-optimized large language model developed by NVIDIA, derived from Nemotron-3-Super-120B-A12B. The model is produced using Iterative Puzzle, a post-training compression framework, with the goal of significantly improving inference efficiency for interactive, reasoning-heavy, and long-context workloads while preserving strong downstream accuracy. The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super, it supports Multi-Token Prediction (MTP) for faster text generation. Compared to its parent, Puzzle-75B-A9B reduces the model from 120.7B total / 12.8B active parameters to 75.3B total / 9.3B active parameters. See the tech report for full training and compression details: Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs. Compared to Nemotron-3-Super, Puzzle-75B-A9B: Achieves approximately 2× higher server throughput on a single 8×B200 node at matched user-throughput constraints, Increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests, Maintains strong accuracy across reasoning, coding, multilingual, long-context, and agentic benchmarks. The supported languages include: English, French, German, Italian, Japanese, Spanish, and Chinese. This model is ready for commercial use. License/Terms of Use Governing Download Terms: Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). This project is currently not accepting contributions. Benchmarks All evaluation results were collected via Nemo Evaluator SDK and for most benchmarks, the Nemo Skills Harness. For reproducibility purposes, more details on the evaluation settings can be found in the Nemo Evaluator SDK configs folder and the reproducibility tutorial for Nemotron 3 Super. The open source container on Nemo Skills packaged via NVIDIA's Nemo Evaluator SDK used for evaluations can be found here. In addition to Nemo Skills, the evaluations also used dedicated open-source packaged containers for Tau-2 Bench (default prompt), Terminal Bench Hard (48 tasks), ScaleAI Multi Challenge Multi-

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-3-puzzle for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (nvidia-nemotron-labs-3-puzzle 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-3-puzzle","messages":[{"role":"user","content":"Hello"}]}'

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