Postulate: Context compaction is good for Quantized models

<!-- SC_OFF --><div class="md"><p>What are your thoughts/what is the consensus on local context compression model size?</p> <p>Are you guys using small MoE models to do this

<!-- 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

<!-- SC_OFF --><div class="md"><p>How do you guys deal with long context, for example while coding, when you’re going back and forth for adjustments or fixing some errors and

<!-- SC_OFF --><div class="md"><p>The news about Ulysses Sequence Parallelism enabling million-token contexts is fascinating for local LLMs. While the potential for deeper context

<!-- SC_OFF --><div class="md"><p>Is this possible for Qwen3.8-27B in LM Studio? </p> <p>Something similar to what ChatGPT/Codex does. I use that as an example because it does it

<!-- 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

<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>Every output token the model generates requires reading the entire KV cache from High Bandwidth Memory.</p> <p>Here's what that actually looks