Blog(2)

• On the Importance of a Multi-Scale Calibration for Quantization

  With the widespread adoption of Large Language Models (LLMs), quantization techniques have become essential for efficient inference across both on-device environments and large-scale server clusters.

• [CVPR 2023 Series #3] GENIE: Show Me the Data for Quantization

  Since the scale of the state-of-the-art AI models has become deeper, model compression also has been attracting more attention as a method to let models be deployed on edge devices without accessing cloud servers.

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Publications(28)

• Two-Stage Grid Optimization for Group-wise Quantization of LLMs

  AuthorGukryeol Lee, Jeewook Kim, Yongkweon Jeon

  PublishedIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

  Date2026-05-04

• On the Importance of a Multi-Scale Calibration for Quantization

  AuthorIngyu Seong, Hyemi Jang, Yongkweon Jeon

  PublishedIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

  Date2026-05-04

• TurboBoA: Faster and Exact Attention-aware Quantization without Backpropagation

  AuthorYeo Jeong Park, Chungman Lee, Ho-young Kim, Joonyoung Kim, Yongkweon Jeon

  PublishedInternational Conference on Learning Representations (ICLR)

  Date2026-04-23

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News(5)

• [Blog] On the Importance of a Multi-Scale Calibration for Quantization

  With the widespread adoption of Large Language Models (LLMs), quantization techniques have become essential for efficient inference across both on-device environments and large-scale server clusters.

• [Blog] LittleBit: Ultra Low-Bit Quantization via Latent Factorization

  Large Language Models (LLMs) have transformed technology, but their massive size presents a significant barrier to deployment. The huge memory and computational costs, especially GPU VRAM, make it difficult to run these powerful models on consumer-grade or edge devices.

• [Blog] A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision Quantization

  Research in Automatic Speech Recognition (ASR) continues to show that larger models yield better results. But while state-of-the-art networks continue to grow with billions of parameters, the difficulty of deploying these models on device also increases.

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