How do you experiment with a (very) large model architecture? [D]

<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>Hi everyone,</p> <p>I’m currently working on a project to build a <strong>100% local AI chatbot</strong> for a government-related use case

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

<!-- SC_OFF --><div class="md"><p>I need SOTA ai at like 2k TPS with tiny latency so that I can get time to first answer token under 3 seconds for real time replies with full COT

<!-- SC_OFF --><div class="md"><p>Just a theoretical question --</p> <p>say you have access to the implementation, including all the weights, of a frontier model like GPT 5.5 or

<!-- SC_OFF --><div class="md"><p>Hey everyone, I’m currently an ML Engineer looking to pick up iOS development, and I’m upgrading my hardware to handle both. I’m moving away from

<!-- SC_OFF --><div class="md"><p>everyone's focused on the benchmarks (14 state of the art evals, beats mythos on terminal-bench, etc) but i think the actual story of 5.5 is what