AMAImedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-BF16

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Apr 15, 2026License:nvidia-open-model-licenseArchitecture:Transformer0.0K Open Weights Cold

AMAImedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-BF16 is an 8 billion parameter Qwen3-based decoder-only transformer model, derived from nvidia/Nemotron-Orchestrator-8B. This BF16 reference checkpoint, developed by AMAImedia, is a lossless cast from the original FP32 release, designed to reduce download bandwidth and disk footprint for research and development. It serves as the English orchestration teacher within the NOESIS Professional Multilingual Dubbing Automation Platform, providing a clean BF16 baseline for downstream quantization recipes.

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Overview

This model, AMAImedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-BF16, is an 8 billion parameter, BF16 reference checkpoint of the nvidia/Nemotron-Orchestrator-8B model. It is based on the Qwen3-8B underlying architecture, which is a dense, decoder-only transformer. Developed by AMAImedia as part of the NOESIS Professional Multilingual Dubbing Automation Platform, this release provides a bandwidth-friendly alternative to the original FP32 model.

Key Characteristics

  • Precision: BF16 (bfloat16) — a lossless cast from the original FP32, maintaining the same 8-bit exponent range as FP32.
  • Efficiency: Halves download bandwidth and disk footprint (~16 GB vs ~32 GB) compared to the FP32 version, and skips a slow load-time cast for users.
  • Architecture: Qwen3-8B, a dense (not MoE) decoder-only transformer.
  • Context: Inherits the capabilities of the Nemotron-Orchestrator-8B base model, which is designed for tool orchestration.
  • License: Inherits the NVIDIA Open Model License, designated "for research and development only."

Use Cases

  • Research and Development: Provides a clean BF16 baseline for experimenting with downstream quantization recipes and inference.
  • NOESIS Platform: Serves as the English orchestration teacher for NOESIS Specialist M9-ORCH-4B during knowledge distillation within the NOESIS multilingual dubbing automation platform.
  • Efficient Deployment: Ideal for users seeking reduced model size and faster loading times without sacrificing inference quality, especially on GPUs with 24 GB+ VRAM for full-resident inference.

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