Coding Diffusion Gemma from scratch

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1u3f9hs/talk_text_diffusion_google_deepminds_brendan/"> <img alt="[Talk] Text Diffusion — Google DeepMind's

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1w2aupi/implementing_kimi_k3_from_scratch_in_pytorch_p/"> <img alt="Implementing Kimi K3 from scratch

&#32; submitted by &#32; <a href="https://www.reddit.com/user/Winter_Mistake_3185"> /u/Winter_Mistake_3185 </a> <br /> <span><a

<p>Today Inception Labs dropped <strong>Mercury 3</strong>: a diffusion-based LLM with a 1M-token context window doing <strong>1,068 tokens/sec on NVIDIA GPUs</strong>, claiming

<!-- SC_OFF --><div class="md"><p>Peter Holderrieth and Ezra Erives just released their new MIT 2026 course on flow matching and diffusion models! It introduces the full stack of

<!-- SC_OFF --><div class="md"><p>Google DeepMind dropped Gemma 4 today:</p> <p><strong>Gemma 4 31B:</strong> dense, 256K context, redesigned architecture targeting efficiency and

<!-- SC_OFF --><div class="md"><p>I recently finished <em>The Principles of Diffusion Models</em>, and honestly I think it’s exceptional.</p> <p>The authors strike a really good

<!-- SC_OFF --><div class="md"><p>I’m not a machine learning expert or anything, but I do enjoy learning about how it all works. I’ve noticed that one of the main limitations of