I ran Muse Glimmer @ 1M context - All tests passed.

<!-- SC_OFF --><div class="md"><p>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out

<!-- SC_OFF --><div class="md"><blockquote> <p><strong>WARNING: wait before download from HF: I just realised my upload of the new versions with the additional fix in the chat

<!-- 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

<!-- SC_OFF --><div class="md"><p>Hey everyone, I've been working on getting Multi-Token Prediction (MTP) working with quantized GGUFs for Qwen3-27B and the results are pretty

<ul> <li> <strong>Book:</strong> <a href="https://www.amazon.com/dp/B0GX2SQ594" rel="noopener noreferrer">System Design Pocket Guide: Interviews</a> </li> <li> <strong>Also by

<!-- SC_OFF --><div class="md"><p>I run Gemma 4 26B-A4B locally via Ollama as part of a custom self-hosted AI platform. The platform stores every model interaction in SQLite,

<!-- SC_OFF --><div class="md"><p>That foodtruck bench post showing deepseek v4 matching gpt-5.2 at 17x cheaper got me thinking. if frontier cloud models are that overpriced for

<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