---
format: "aidr-story-markdown/v1"
id: "26a2930736bab1dacce62d6979f0c1082233b4ce0e99908d9f63fa0fb18ad09d"
canonical_url: "https://aidr.today/26a29307?lang=en"
title: "SoloQ: Calibration-Free Quantization for Diffusion Language Models"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
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published_at: "2026-10-12T00:00:00.000Z"
category: "Research"
topics: ["inference"]
source_urls: ["https://huggingface.co/papers/2610.07121"]
summary: "SoloQ is a calibration-free quantization framework for diffusion large language models (dLLMs). It maps weights and activations into a normalized rotated basis to enable data-independent quantization."
---

# SoloQ: Calibration\-Free Quantization for Diffusion Language Models

> [Open the canonical story](<https://aidr.today/26a29307?lang=en>)

**Published:** 2026-10-12T00:00:00.000Z
**Category:** Research
**Topics:** inference

## Summary

SoloQ is a calibration\-free quantization framework for diffusion large language models \(dLLMs\)\. It maps weights and activations into a normalized rotated basis to enable data\-independent quantization\.

## Sources

- [Story source](<https://huggingface.co/papers/2610.07121>)