Blog(5)

• RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs

  The rapid advancement of Large Language Model (LLM) capabilities has been accompanied by a significant increase in model parameter scale.

• LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment

  Large Language Models (LLMs) continue to deliver remarkable capabilities, but their memory footprint remains a rigid barrier to deployment.

• 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.

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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(6)

• [Blog] NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models

  Weight-only quantization of Large Language Models (LLMs) aims to make LLM serving efficient by reducing parameter precision.

• [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.

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