Excellent discussion about LLM scaling [D]

Reddit r/MachineLearning / 5/4/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

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Key Points

The article shares an in-depth discussion of how memory and compute requirements scale for large language models (LLMs).
It argues that using LLMs locally or on private cloud can be inefficient, depending on how scaling behavior interacts with your workload.
A key takeaway is that inference becomes more efficient when using large batching strategies, enabled by favorable memory/compute scaling.
The piece recommends a linked discussion video featuring Reiner Pope about how GPT, Claude, and Gemini are trained and served.

I came across an excellent in depth discussion of memory and compute scaling analysis for LLMs. One takeaway is that running LLMs locally or on private cloud is wasteful. Memory / compute scaling makes large batching during inference very efficient.

Highly recommend. How GPT, Claude, and Gemini are actually trained and served with Reiner Pope

submitted by /u/geneing
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