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

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Apr 15, 2026Architecture:Transformer0.0K Featherless Exclusive Cold

The AMAImedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-BF16 is an 8 billion parameter Qwen3-derived model, specifically a BF16 reference checkpoint of NVIDIA's Nemotron-Orchestrator-8B. Developed by AMAImedia as part of the NOESIS Professional Multilingual Dubbing Automation Platform, this model is designed for orchestration tasks and supports 119 languages. It provides a bandwidth-friendly BF16 baseline, halving download size and disk footprint compared to the original FP32 release, making it efficient for research and development in multilingual AI applications.

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Model Overview

This model, AMAImedia/Qwen3-8B-Nemotron-Orchestrator-NOESIS-BF16, is an 8 billion parameter Qwen3-derived model, specifically a BF16 (BFloat16) reference checkpoint of the nvidia/Nemotron-Orchestrator-8B base model. Developed by AMAImedia as part of the NOESIS Professional Multilingual Dubbing Automation Platform, it is designed for orchestration tasks within the DHCF-FNO framework.

Key Characteristics

  • Base Architecture: Qwen3-8B (decoder-only transformer, dense).
  • Precision: BF16, losslessly cast from the original FP32 release of Nemotron-Orchestrator-8B.
  • Efficiency: Halves download bandwidth (~16 GB vs ~32 GB) and disk footprint, and skips slow load-time casting for users.
  • Multilingual Support: Inherits Qwen3's extensive language coverage, supporting 119 languages and dialects, including English, Chinese, Arabic, Spanish, French, and many others.
  • Orchestration Focus: Serves as the English orchestration teacher for NOESIS Specialist M9-ORCH-4B during knowledge distillation.

Important Considerations

  • License: Inherits the NVIDIA Open Model License from the base model, which designates it "for research and development only." Users are responsible for compliance.
  • VRAM: Requires approximately ~17 GB VRAM for full-resident BF16 inference on GPUs with 24 GB+.

Good for

  • Researchers and developers working on multilingual AI applications, particularly in dubbing automation.
  • Users seeking an efficient, pre-cast BF16 baseline for downstream quantization or inference.
  • Projects requiring a robust orchestration model with broad language support.

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