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LatestNVIDIA1
NVIDIAAny-to-Any

NVIDIA's Audex Unifies Audio Understanding and Speech

A new 30B mixture-of-experts model from NVIDIA handles both listening and speaking within a single audio-text architecture.

Jul 6, 2026
NotableOther
Nemotron-Labs-Audex-30B-A3B

NVIDIA has released Nemotron-Labs-Audex-30B-A3B, a mixture-of-experts model that folds audio understanding and speech generation into a single system. According to the model's Hugging Face page, it is designed as a unified audio-text architecture — able to both interpret incoming audio and produce speech, rather than splitting those jobs across separate specialized models.

The model uses a mixture-of-experts design with roughly 30 billion total parameters but only about 3 billion active per token, the arrangement its "30B-A3B" name signals. That approach keeps inference costs closer to a small dense model while giving the network a larger pool of specialized capacity to draw from, a pattern NVIDIA and others have leaned on across recent Nemotron releases.

Why it matters

Most production audio stacks still chain together distinct components — a speech recognizer, a language model, and a separate text-to-speech engine. A single model spanning both comprehension and generation could simplify those pipelines and reduce the latency and error accumulation that comes from stitching parts together.

  • Unified audio-text handling for both understanding and speech synthesis
  • Mixture-of-experts efficiency: ~30B total, ~3B active parameters
  • Reasoning listed among its capabilities alongside audio and TTS

The release ships under a custom NVIDIA license rather than a standard open-source one, so teams will want to check the terms before building on it. As an initial version, Audex sets a baseline for a family that NVIDIA appears positioned to iterate on.

Sources

  • nvidia/Nemotron-Labs-Audex-30B-A3B

    Hugging Face

    Visit
OlderGigaChat 3.5 arrives as a 432B mixture-of-experts modelAi Sage · Text / LLM · 4 months agoNewerLingBot-Video puts a 30B MoE behind embodied AI videorobbyant · Text → Video · 4 months ago

Get the model

Hugging Face

Specs

Parameters30B · MoE
Context window1.0M tokens
Architecturenemotron_labs_audex
LicenseOTHER
Downloads545
Likes187

Can you run it?

Runs on a 24 GB GPU at 4-bit.

  • BF16 (as published)

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    65.7 GB
  • 8-bit

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    35.6 GB
  • 4-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    20.9 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 32B parameters must be resident even though only 3B are active per token — active count affects speed, not memory.
  • Precision not published; assuming 16-bit (BF16) weights.
  • KV cache approximated from model size (no config metadata) at 8,192 tokens.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-AnyText → SpeechReasoning

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