Update author on blog post (#1920)

cjyabraham · web-flow · commit 2e2e2f052dea · 2025-02-20T09:39:56.000+07:00
Signed-off-by: Chris Abraham &lt;cjyabraham@gmail.com&gt;
diff --git a/_community_blog/optimize-llms.md b/_community_blog/optimize-llms.md
@@ -2,6 +2,7 @@
 title: "Optimize LLMs for Efficiency & Sustainability"
 ext_url: /blog/optimize-llms/
 date: Feb 19, 2025
+author: "Zach Lasiuk, Arm"
 ---
 
 The rapid growth of large language model (LLM) applications is linked to rapid growth in energy demand. According to the International Energy Agency (IEA), data center electricity consumption is projected to roughly double by 2026 primarily driven by AI. This is due to the energy-intensive training requirements for massive LLMs – however, the increase in AI Inferencing workloads also plays a role. For example, compared with traditional search queries, a single AI inference can consume about [10x more energy](https://www.weforum.org/stories/2024/07/generative-ai-energy-emissions/).
diff --git a/_posts/2025-02-19-optimize-llms.md b/_posts/2025-02-19-optimize-llms.md
@@ -2,6 +2,7 @@
 layout: blog_detail
 title: "Optimize LLMs for Efficiency & Sustainability"
 hidden: true
+author: "Zach Lasiuk, Arm"
 ---
 
 The rapid growth of large language model (LLM) applications is linked to rapid growth in energy demand. According to the International Energy Agency (IEA), data center electricity consumption is projected to roughly double by 2026 primarily driven by AI. This is due to the energy-intensive training requirements for massive LLMs – however, the increase in AI Inferencing workloads also plays a role. For example, compared with traditional search queries, a single AI inference can consume about [10x more energy](https://www.weforum.org/stories/2024/07/generative-ai-energy-emissions/).
