Long Context Models Memory Efficiency

<!-- SC_OFF --><div class="md"><p>Been building AI agents for a while and kept hitting the same wall: agents that work great at the start of a session and quietly fall apart by

<p>The quadratic attention problem has quietly shaped everything you've built with LLMs. RAG pipelines, agentic decomposition, hybrid architectures — these aren't the natural

<!-- SC_OFF --><div class="md"><p><strong>Noam Brown</strong> — OpenAI Research Scientist and co-creator of o1, Libratus, and Cicero — sat down with Dwarkesh Patel for a 1h 20m

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>Running Llama-3-70B with long contexts and hitting HBM limits constantly. 128K context burns approx 40GB just for KV cache — half the H100 gone

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2bne8/context_shifting_sliding_window_rag/"> <img alt="Context Shifting + sliding window + RAG"

<!-- SC_OFF --><div class="md"><p>Link to the paper:</p> <p><a