transformer architecture

Transformer Architecture Explained: The Engine Behind Modern AI

A deep dive into the transformer architecture that powers everything from ChatGPT to AI video generators. Understanding attention mechanisms and why this design revolutionized machine learning.

LLM

New KV Cache Method Enables Sublinear Memory Growth for LLMs

Researchers introduce Adaptive Soft Rolling KV Freeze with entropy-guided recovery, achieving sublinear memory scaling for long-context LLM inference without significant quality loss.

mechanistic interpretability

GPT-2 Dissected: How Transformer Layers Process Sentiment

New research reveals how GPT-2's layers divide labor between lexical and contextual processing during sentiment analysis, advancing our understanding of transformer internals.

AI infrastructure

Q-Filters Achieves 32x KV Cache Compression for AI Models

New Q-Filters technique compresses transformer KV cache by 32x while maintaining model performance, dramatically reducing memory requirements for large language models and video generation systems through innovative quantization methods.

LLM Inference

How KV Cache Accelerates LLM Inference Performance

Deep dive into the Key-Value cache mechanism that enables fast language model inference, exploring memory optimization strategies and architectural decisions that power modern AI systems including video generation models.