May 16, 2026

The Core Principles of KV Cache Compression: Turning Information Loss into Intelligent Filtering

Large Language Models (LLMs) generate responses by storing and utilizing information from previous tokens, a process that produces a collection of Key and Value vectors known as the "KV Cache" [S2458]. As context length

LLMKV CacheMemory OptimizationAttention Matching+1

May 7, 2026

The Trap of Perfect Data: Strategic Information Loss for Intelligent Modeling

While there is a close relationship between data volume and model performance, unconditional data accumulation does not necessarily guarantee optimal intelligence. Traditionally, acquiring more training data was consider

Data ModelingKnowledge DistillationLLM OptimizationAI Cost Reduction+1

May 5, 2026

The Next-Gen Engine for LLMs, Ring Attention: A Technical Breakthrough in Conquering Long Contexts

Recent advancements in LLMs have seen context windows explode—from GPT-4 Turbo (128K) to Gemini 1.5 Pro (1M+)—making long-context processing a core competitive advantage [S2439]. However, amidst this technological surge,

LLMRing AttentionKV CacheLong Context+1

May 5, 2026

The Physical Limits of KV Cache and VRAM: Why Infinite Context is Impossible

When working with Large Language Models (LLMs) to handle long documents, we frequently encounter situations where increasing the prompt length leads to an explosive rise in VRAM usage, eventually causing the system to st

LLMKV CacheVRAMLong Context+1

May 4, 2026

SwiftKV: Understanding the Principles of Next-Generation KV Cache Compression for Maximizing LLM Inference Efficiency

At their core, Large Language Models (LLMs) are auto-regressive models that predict the next token based on preceding ones. This inherent characteristic leads to a structural problem: every time a new token is generated,

LLMKV CacheSwiftKVMemory Optimization+2