8 papers · 1 filter
Tailoring the Quantization Space for 1-Bit KV Cache Compression
Minsoo Cheong, Donghyun Son, Sungjoo Yoo
The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwidth. To mitigate this bottleneck…
Output-Aware Rotation for INT2 KV-Cache Quantization
Vincent-Daniel Yun, Woosang Lim, Minsoo Cheong +4
The key-value (KV) cache has become a major memory and bandwidth bottleneck in long-context large language model inference, making ultra-low-bit quantization increasingly important…
LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss
Euntae Choi, Sumin Song, Sungjoo Yoo
Large reasoning models (LRMs) reach competition-level math and coding accuracy via long autoregressive decoding, making per-token decoding cost a primary deployment concern. Weight…
Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training
Myeonghwan Ahn, Sungjoo Yoo
Ever-growing scale of large language models (LLMs) is pushing for improved efficiency, favoring fully quantized training (FQT) over BF16. While FQT accelerates training, it faces c…
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache
Donghyun Son, Euntae Choi, Sungjoo Yoo
Large Language Model (LLM) inference is typically memory-intensive, especially when processing large batch sizes and long sequences, due to the large size of key-value (KV) cache.…
Grouped Sequency-arranged Rotation: Optimizing Rotation Transformation for Quantization for Free
Euntae Choi, Sumin Song, Woosang Lim +1
Large Language Models (LLMs) face deployment challenges due to high computational costs, and while Post-Training Quantization (PTQ) offers a solution, existing rotation-based metho…
Rotate, Clip, and Partition: Towards W2A4KV4 Quantization by Integrating Rotation and Learnable Non-uniform Quantizer
Euntae Choi, Sumin Song, Woosang Lim +1
We propose Rotate, Clip, and Partition (RCP), a quantization-aware training (QAT) approach that first realizes extreme compression of LLMs with W2A4KV4(2-bit weight, 4-bit activati…
Breaking MLPerf Training: A Case Study on Optimizing BERT
Yongdeok Kim, Jaehyung Ahn, Myeongwoo Kim +11
Speeding up the large-scale distributed training is challenging in that it requires improving various components of training including load balancing, communication, optimizers, et…