‹ BackHN Continuity

Thread

Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

169 points · 65 comments · pythonic_hell

  1. jmiskovic · · focus · HN ↗
    Incredibly important research. We've reached the point where local LLMs are good enough! It takes less time for local model to take the first action on your task than it does for Claude to validate your login, put you into queue and start issuing the commands. Local models are persistent and 100% predictable unlike any cloud offering. It's better for the power system for the demand to be distributed. During the winter time the GPU also doubles as a 300W in-house heater. Not to mention avoiding personal data collection and re-selling.
    1. Aurornis · · focus · HN ↗
      > We've reached the point where local LLMs are good enough!

      For some tasks, yes. For most of my deeper work they're not even close to my subscriptions.

      > It takes less time for local model to take the first action on your task than it does for Claude to validate your login, put you into queue and start issuing the commands.

      I have some decent LLM hardware here and I strongly disagree with this. Claude responds quickly. Using Fable or Opus it will deliver a working result faster than my local models because it gets there in fewer tokens. That's just how it is.

      > During the winter time the GPU also doubles as a 300W in-house heater.

      This is a curse in the summer. I'm feeling it right now.

Open on Hacker News to reply ↗

Unofficial Hacker News client; not affiliated with Y Combinator.