HomeNVIDIA Unveils Nemotron-Labs-Audex-30B-A3B Unified Audio Intelligence Model
As multimodal large models evolve rapidly, audio processing capabilities are frequently compromised—many models improve audio understanding at the expense of text logic. To address this, NVIDIA researchers have introduced Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text large language model designed to bridge this gap.
Audex employs a streamlined and efficient design, built on a robust pure-text Mixture of Experts (MoE) architecture. By utilizing a single Transformer decoder, it processes text and quantized audio tokens simultaneously. This approach projects audio inputs into the text embedding space, ensuring seamless integration with existing LLM infrastructure for multimodal tasks and achieving true deep integration.
Training involved extensive datasets comprising 157.4 billion audio tokens and 320.5 billion text tokens. Through multi-stage supervised training, pure-text Cascade Reinforcement Learning, and multi-domain in-policy knowledge distillation, Audex achieves industry-leading performance in audio understanding, speech recognition, translation, and generation. Crucially, it preserves the original LLM’s core capabilities in reasoning, alignment, knowledge retrieval, and long-text processing with minimal degradation.
As an open-source model, Audex marks a significant milestone for the voice technology sector. Moving beyond theoretical research, it offers a mature solution for developers to evaluate and deploy directly. For those managing complex audio interactions, Audex provides a balanced option that enhances performance while opening new avenues for multimodal intelligence research.
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As multimodal large models evolve rapidly, audio processing capabilities are frequently compromised—many models improve audio understanding at the expense of text logic. To address this, NVIDIA researchers have introduced Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text large language model designed to bridge this gap.
Audex employs a streamlined and efficient design, built on a robust pure-text Mixture of Experts (MoE) architecture. By utilizing a single Transformer decoder, it processes text and quantized audio tokens simultaneously. This approach projects audio inputs into the text embedding space, ensuring seamless integration with existing LLM infrastructure for multimodal tasks and achieving true deep integration.
Training involved extensive datasets comprising 157.4 billion audio tokens and 320.5 billion text tokens. Through multi-stage supervised training, pure-text Cascade Reinforcement Learning, and multi-domain in-policy knowledge distillation, Audex achieves industry-leading performance in audio understanding, speech recognition, translation, and generation. Crucially, it preserves the original LLM’s core capabilities in reasoning, alignment, knowledge retrieval, and long-text processing with minimal degradation.
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