• Seattle Skeptics on AI
Seattle Skeptics on AI

Tag: inference optimization

Memory and Compute Footprints of Transformer Layers in Production LLMs
Memory and Compute Footprints of Transformer Layers in Production LLMs

Tamara Weed, Feb, 24 2026

Understanding memory and compute footprints in transformer layers is critical for deploying LLMs efficiently. KV cache, quantization, and attention optimizations determine cost, speed, and reliability in production.

Categories:

Science & Research

Tags:

transformer layers LLM memory footprint KV cache inference optimization transformer compute

Recent post

  • Vibe Coding for Knowledge Workers: Tools That Save Hours Every Week
  • Vibe Coding for Knowledge Workers: Tools That Save Hours Every Week
  • Context Windows in Large Language Models: Limits, Trade-Offs, and Best Practices
  • Context Windows in Large Language Models: Limits, Trade-Offs, and Best Practices
  • Data Privacy for Generative AI: Minimization, Retention, and Anonymization
  • Data Privacy for Generative AI: Minimization, Retention, and Anonymization
  • Generative AI in Business Operations: High-Impact Use Cases and Real Implementation Patterns
  • Generative AI in Business Operations: High-Impact Use Cases and Real Implementation Patterns
  • Chain-of-Thought in Vibe Coding: Why Explanations Before Code Work Better
  • Chain-of-Thought in Vibe Coding: Why Explanations Before Code Work Better

Categories

  • Science & Research

Archives

  • February 2026
  • January 2026
  • December 2025
  • November 2025
  • October 2025
  • September 2025
  • August 2025
  • July 2025

Tags

vibe coding large language models AI coding tools prompt engineering generative AI LLM security AI governance AI coding AI compliance transformer models AI code security GitHub Copilot LLM deployment AI agents AI implementation data privacy AI development LLM architecture GPU optimization AI in healthcare

© 2026. All rights reserved.