{
  "id": 1547,
  "url": "https://arxiv.org/abs/2607.08526",
  "title": "A Quantized Native Runtime for On-Device Semantic Audio Generation",
  "summary": "Semantic audio applications increasingly require controllable generation on commodity and embedded hardware rather than through framework-heavy datacenter stacks. We present aria, a dependency-free native runtime that runs the complete text-to-music pipeline of Stable Audio~3 (SA3) on ordinary GPUs, CPU-only machines, and a Raspberry~Pi~5, with no Python or deep-learning framework underneath. Our main contribution is a study of quantization: running the model at lower numerical precision to fit ",
  "authors": "Matteo Spanio, Antonio Rodà",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-08T20:00:00.000Z",
  "fetched_at": "2026-07-14T16:04:12.223Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/1547",
  "original_url": "https://arxiv.org/abs/2607.08526",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}