13 posts are grouped under this topic.
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
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,
Today, AI technology has achieved revolutionary progress in understanding and generating human language thanks to the emergence of Large Language Models (LLMs) like GPT and Claude [S2366]. However, while these massive mo
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
As the global AI race intensifies, the focus is shifting beyond mere technical performance toward "governance frameworks"—how we manage and operate these models—becoming a key factor for survival. Current Large Language
As LLM capabilities advance, models are becoming increasingly intelligent; however, when applying them to real-world services, we constantly encounter the physical limitation of "speed." Since LLMs generate text sequenti
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,
Large Language Models (LLMs), which sit at the heart of recent AI advancements, are revolutionizing various industries by generating human-like text based on vast amounts of data [S2225]. However, when deploying these mo
The recently released DeepSeek V4 has sent shockwaves through the industry with its staggering 1.6 trillion parameter scale, yet its output cost is priced at a mere $3.48—roughly one-tenth of GPT-5.5 [S957]. Conventional
In the rapidly evolving field of Large Language Models (LLMs), securing high-quality reasoning data has emerged as a key driver of performance, arguably as much as increasing parameter counts. In particular, data contain
Explore how moving Large Language Models from cloud servers to the user's browser solves critical data security and latency issues. This post examines how local execution ensures that sensitive user data never leaves their machine while providing instant response times.
Explore how the combination of WebGPU and browser-based engines allows users to run large language models entirely on their local hardware. This shift moves computation from remote servers directly to the client's GPU, ensuring high performance without traditional cloud overhead.
Explore how DeepSeek's DualPath architecture addresses the critical bottleneck of storage bandwidth during LLM inference. This technical deep dive examines how optimizing data flow enables more efficient agentic workflows.