In long-context reasoning with large language models, the KV cache becomes bloated, creating a memory bottleneck. This paper focuses on how quantization errors accumulate during decoding, particularly ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...

今回紹介する論文はこちら。 大規模言語モデルの長文推論では、KVキャッシュが肥大化してメモリがボトルネックになります。本論文は、量子化誤差がデコード中に蓄積し、特に推論や長文生成で品質を落とす点に注目し、その挙動を分析したうえで改善策 ...

The difference between an analog wave and its digital representation. Also known as "quantization noise." See quantization. THIS DEFINITION IS FOR PERSONAL USE ONLY. All other reproduction requires ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

advanced-gguf-quantizer is a llama.cpp-derived, CUDA accelerated GGUF quantization toolkit for creating, inspecting, evaluating, improving, and testing GGUF models. The first focus initially was ...

description [ICLR 2026][Model Compression][LLM Quantization] SERQ unifies activation outliers and weight saliency into a **single** low-rank compensation matrix ...